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• Neural Networks.- Pattern Recognition.- Image Processing.- Intelligent Computing in Robotics.- Intelligent Control and Automation.- Intelligent Data Analysis and Prediction.- Fuzzy Theory and Algorithms.- Supervised Learning.- Unsupervised Learning.- Kernel Methods and Supporting Vector Machines.- Knowledge Discovery and Data Mining.- Natural Language Processing and Computational Linguistics.- Gene Expression Array Analysis.- Systems Biology.- Computational Genomics.- Computational Proteomics.- Gene Regulation Modeling and Analysis.- Protein-Protein Interaction Prediction.- Next-Gen Sequencing and Metagenomics.- Structure Prediction and Folding.- Evolutionary Optimization for Scheduling.- High-Throughput Biomedical Data Integration and Mining.- Machine Learning Algorithms and Applications.- Heuristic Optimization Algorithms for Real-World Applications.- Evolutionary Multi-Objective Optimization and Its Applications.- Swarm Evolutionary Algorithms for Scheduling and Combinatorial.- Optimization.- Swarm Intelligence and Applications in Combinatorial Optimization.- Advances in Metaheuristic Optimization Algorithm.- Advances in Image Processing and Pattern Recognition Techniques.- AI in Biomedicine.- Bioinformatics.- Biometrics Recognition.- Information Security.- Virtual Reality and Human-Computer Interaction.- Healthcare Informatics Theory and Methods.- Intelligent Computing in Computer Vision.- Intelligent Agent and Web Applications.- Reinforcement Learning.- Machine Learning.- Modeling, Simulation, and Optimization of Biological Systems.- Biomedical Data Modeling and Mining.- Cheminformatics.- Intelligent Computing in Computational Biology.- Protein Structure and Function Prediction.- Biomarker Discovery.- Hybrid Computational Intelligence: Theory and Application in Bioinformatics, Computational Biology and Systems Biology.- IoT and Smart Data.- Intelligent Systems and Applications for Bioengineering.- Evolutionary Optimization: Foundations and Its Applications to Intelligent Data Analytics.- Protein and Gene Bioinformatics: Analysis, Algorithms and Applications.
• (source: Nielsen Book Data)

This two-volume set LNCS 10954 and LNCS 10955 constitutes - in conjunction with the volume LNAI 10956 - the refereed proceedings of the 14th International Conference on Intelligent Computing, ICIC 2018, held in Wuhan, China, in August 2018. The 275 full papers and 72 short papers of the three proceedings volumes were carefully reviewed and selected from 632 submissions. The papers are organized in topical sections such as Neural Networks.- Pattern Recognition.- Image Processing.- Intelligent Computing in Robotics.- Intelligent Control and Automation.- Intelligent Data Analysis and Prediction.- Fuzzy Theory and Algorithms.- Supervised Learning.- Unsupervised Learning.- Kernel Methods and Supporting Vector Machines.- Knowledge Discovery and Data Mining.- Natural Language Processing and Computational Linguistics.- Gene Expression Array Analysis.- Systems Biology.- Computational Genomics.- Computational Proteomics.- Gene Regulation Modeling and Analysis.- Protein-Protein Interaction Prediction.- Next-Gen Sequencing and Metagenomics.- Structure Prediction and Folding.- Evolutionary Optimization for Scheduling.- High-Throughput Biomedical Data Integration and Mining.- Machine Learning Algorithms and Applications.- Heuristic Optimization Algorithms for Real-World Applications.- Evolutionary Multi-Objective Optimization and Its Applications.- Swarm Evolutionary Algorithms for Scheduling and Combinatorial.- Optimization.- Swarm Intelligence and Applications in Combinatorial Optimization.- Advances in Metaheuristic Optimization Algorithm.- Advances in Image Processing and Pattern Recognition Techniques.- AI in Biomedicine.- Bioinformatics.- Biometrics Recognition.- Information Security.- Virtual Reality and Human-Computer Interaction.- Healthcare Informatics Theory and Methods.- Intelligent Computing in Computer Vision.- Intelligent Agent and Web Applications.- Reinforcement Learning.- Machine Learning.- Modeling, Simulation, and Optimization of Biological Systems.- Biomedical Data Modeling and Mining.- Cheminformatics.- Intelligent Computing in Computational Biology.- Protein Structure and Function Prediction.- Biomarker Discovery.- Hybrid Computational Intelligence: Theory and Application in Bioinformatics, Computational Biology and Systems Biology.- IoT and Smart Data.- Intelligent Systems and Applications for Bioengineering.- Evolutionary Optimization: Foundations and Its Applications to Intelligent Data Analytics.- Protein and Gene Bioinformatics: Analysis, Algorithms and Applications.
(source: Nielsen Book Data)

• Intro
• Preface
• Organization
• Contents
• Constraint Solving and Optimization
• A Method for the Online Construction of the Set of States of a Markov Decision Process Using Answer Set Programming
• 1 Introduction
• 2 Background
• 2.1 MDP and Reinforcement Learning
• 2.2 Answer Set Programming
• 3 Online ASP for MDP: oASP(MDP)
• 4 Tests and Results
• 4.1 First Test: Changes in the Wall-Free-Space Ratio
• 4.2 Second Test: Changes in the Transition Probabilities
• 5 Related Work
• 6 Conclusion
• References

• Hardware/Software Co-design for Template Matching Using Cuckoo Search Optimization
• 1 Introduction
• 2 Theoretical Background
• 3 Hardware Architecture
• 4 Results
• 5 Conclusion
• References
• Optimziation Methods for Beacon Based Foraging Algorithms
• 1 Introduction
• 2 Related Work
• 3 The Rostering Technique
• 4 Rostering Experimental Analysis
• 5 Network Optimizaiton
• 6 NetOpt Experimental Analysis
• 7 Future Work
• 8 Conclusions
• References
• On Using ``Stochastic Learning on the Line'' to Design Novel Distance Estimation Methods
• 1 Introduction

• 2 Distance Estimation: Core Concepts
• 2.1 Distance Estimation Functions (DEFs)
• 3 The Adaptive Tertiary Search and Its Use in DE
• 3.1 Updating Search Intervals
• 3.2 The Corresponding LA
• 3.3 The Corresponding Environment
• 4 Testing and Results: 2-Dimensional Environments
• 5 Conclusions
• References
• Data Mining and Knowledge Discovery
• Person Re-identification Using Masked Keypoints
• 1 Introduction
• 2 Person Re-identification
• 3 A Keypoint-Based Re-ID Method
• 3.1 Person Detection
• 3.2 Person Re-identification
• 4 Experiments
• 4.1 Data Collection

• 4.2 Parameters Setting
• 4.3 Comparing Our Method
• 5 Conclusions
• References
• Knowledge Discovery Process for Detection of Spatial Outliers
• 1 Introduction
• 2 Knowledge Discovery Process for Detection of Spatial Outliers
• 2.1 Data Preparation
• 2.2 Neighborhood Definition
• 2.3 Outlier Detection and Filtering
• 2.4 Neighborhood Description
• 2.5 Group Outlier Detection
• 2.6 Information Analysis
• 3 Proof of Concept
• 4 Conclusion
• References
• Text Modeling Using Multinomial Scaled Dirichlet Distributions
• 1 Introduction

• 2 Multinomial and Dirichlet Compound Multinomial
• 3 Multinomial Scaled Dirichlet (MSD) Distribution
• 4 MSD Mixture Model Learning
• 5 Experimental Results
• 6 Conclusion
• References
• A Comparison of Knee Strategies for Hierarchical Spatial Clustering
• 1 Introduction
• 2 Background
• 2.1 Hierarchical Clustering
• 2.2 Distance Measures
• 2.3 Knee Determination
• 3 Spatial Data and Preparation
• 4 Results
• 5 Conclusion
• References
• Credit Card Default Prediction as a Classification Problem
• Abstract
• 1 Introduction
• 2 Related Works

This book constitutes the thoroughly refereed proceedings of the 31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2018, held in Montreal, QC, Canada, in June 2018. The 53 full papers and 33 short papers presented were carefully reviewed and selected from 146 submissions. They are organized in the following topical sections: constraint solving and optimization; data mining and knowledge discovery; evolutionary computation; expert systems and robotics; knowledge representation, machine learning; meta-heuristics; multi-agent systems; natural language processing; neural networks; planning, scheduling and spatial reasoning; rough sets, Internet of Things (IoT), ubiquitous computing and big data; data science, privacy, and security; inelligent systems approaches in information extraction; and artificial intelligence, law and justice.
(source: Nielsen Book Data)

• Neural Networks
• Neural Computing and Optimization
• Case Based Reasoning and Autonomy-Oriented Computing
• Knowledge Discovery and Data Mining
• Evolutionary Computing and Genetic Algorithms
• Fuzzy Systems and Soft Computing
• A4 Particle Swarm Optimization and Niche Technology
• Swarm Intelligence and Optimization
• Kernel Methods and Support Vector Machines
• Biological and Quantum Computing
• Intelligent Agent and Web Applications
• Intelligent Sensor Networks
• Intelligent Fault Diagnosis
• Intelligent Control and Automation
• Intelligent Data Fusion and Security
• Intelligent Prediction and Time Series Analysis
• Natural Language Processing and Expert Systems
• Intelligent Image/Document Retrievals
• Intelligent Computing in Bioinformatics
• Intelligent Computing in Signal Processing
• Intelligent Computing in Pattern Recognition
• Intelligent Computing in Commiuncation
• Special Session on Computational Intelligence Approaches and Methods for Security Engineering
• Special Session on Intelligent Human-Computer Interactions for Multi-modal and Autonomous Environment
• Special Session on Intelligent Robot Systems Based on Vision Technology
• Other Topics.

This volume, in conjunction with the two volumes LNCS 4681 and LNAI 4682, constitutes the refereed proceedings of the Third International Conference on Intelligent Computing held in Qingdao, China, in August 2007. The conference sought to establish contemporary intelligent computing techniques as an integral method that underscores trends in advanced computational intelligence and links theoretical research with applications.
(source: Nielsen Book Data)

• Session
• 1. Agents.- Modeling Role-Based Agent Team.- Distributed Collaborative Filtering for Robust Recommendations Against Shilling Attacks.- Competition and Coordination in Stochastic Games.- Multiagent-Based Dynamic Deployment Planning in RTLS-Enabled Automotive Shipment Yard.- R-FRTDP: A Real-Time DP Algorithm with Tight Bounds for a Stochastic Resource Allocation Problem.- A Reorganization Strategy to Build Fault-Tolerant Multi-Agent Systems.- Session
• 2. Bioinformatics.- A Multi-site Subcellular Localizer for Fungal Proteins.- Selecting Genotyping Oligo Probes Via Logical Analysis of Data.- Session
• 3. Classification.- Learning the Semantic Meaning of a Concept from the Web.- On Combining Dissimilarity-Based Classifiers to Solve the Small Sample Size Problem for Appearance-Based Face Recognition.- A Novel Approach for Automatic Palmprint Recognition.- ICS: An Interactive Classification System.- Fast Most Similar Neighbor Classifier for Mixed Data.- Performance Measures in Classification of Human Communications.- Cost-Sensitive Decision Trees with Pre-pruning.- Probability Based Metrics for Locally Weighted Naive Bayes.- Recurrent Boosting for Classification of Natural and Synthetic Time-Series Data.- Pattern Classification in No-Limit Poker: A Head-Start Evolutionary Approach.- Session
• 4. Constraint Satisfaction.- Managing Conditional and Composite CSPs.- Multiagent Constraint Satisfaction with Multiply Sectioned Constraint Networks.- Session
• 5. Data Mining.- A Clustering Algorithm Based on Adaptive Subcluster Merging.- Efficient Algorithms for Video Association Mining.- Distributed Data Mining in a Ubiquitous Healthcare Framework.- Constructing a User Preference Ontology for Anti-spam Mail Systems.- Question Answering Summarization of Multiple Biomedical Documents.- A Profit-Based Business Model for Evaluating Rule Interestingness.- Session
• 6. Knowledge Representation and Reasoning.- Reasoning About Operations on Sets.- Analytic Results on the Hodgkin-Huxley Neural Network: Spikes Annihilation.- Improving Importance Sampling by Adaptive Split-Rejection Control in Bayesian Networks.- Adding Local Constraints to Bayesian Networks.- On the Use of Possibilistic Bases for Local Computations in Product-Based Possibilistic Networks.- Reasoning with Conditional Preferences Across Attributes.- Path Propagation for Inference in Bayesian Networks.- Problem-Solving Knowledge Mining from Users' Actions in an Intelligent Tutoring System.- Incremental Neighborhood Graphs Construction for Multidimensional Databases Indexing.- Session
• 7. Learning.- Learning Network Topology from Simple Sensor Data.- Reinforcement Learning in Nonstationary Environment Navigation Tasks.- On the Stability and Bias-Variance Analysis of Kernel Matrix Learning.- Session
• 8. Natural Language.- Query-Based Summarization of Customer Reviews.- Multi-state Directed Acyclic Graphs.- Fuzzy Clustering for Topic Analysis and Summarization of Document Collections.- Creating a Fuzzy Believer to Model Human Newspaper Readers.- Rethinking the Semantics of Complex Nominals.- A Hybrid Approach to Improving Automatic Speech Recognition Via NLP.- Session
• 9. Planning.- Planning in Multiagent Expedition with Collaborative Design Networks.- Hierarchical Shortest Pathfinding Applied to Route-Planning for Wheelchair Users.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 20th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2007, held in Montreal, Canada, in May 2007. The 46 revised full papers cover agents, bioinformatics, classification, constraint satisfaction, data mining, knowledge representation and reasoning, learning, natural language, and planning.
(source: Nielsen Book Data)

• Local Ordinal Classification.- Using Genetic Algorithms and Decision Trees for a posteriori Analysis and Evaluation of Tutoring Practices based on Student Failure Models.- Exploiting Decision Trees in Product-based Fuzzy Neural Modeling to Generate Rules with Dynamically Reduced Dimensionality.- Retraining the Neural Network for Data Visualization.- Rule-Based Adaptive Navigation for an Intelligent Educational Mobile Robot.- BRWM: A relevance feedback mechanism for web page clustering.- Bagged Averaging of Regression Models.- Argument-based User Support Systems using Defeasible Logic Programming.- Knowledge Modelling Using The UML Profile.- Optimized Multi-Domain Secure Interoperation using Soft Constraints.- Learning context models for the recognition of scenarios.- MDA-Based Architecture of a Description Logics Reasoner.- Incremental guideline formalization with tool support.- Accessing data in the semantic web: An intelligent data integration and navigation approaches.- An Expert System Delivery Environment for the World Wide Web.- A Formally Specified Ontology Management API as a Registry for Ubiquitous Computing Systems.- Intelligent Configurable Electronic Shop Platform based on Ontologies and 3D Visualization.- Adapting User Interfaces to the User Interaction Requirements in Real Time.- On supporting young students in visual logic modeling.- Attentional Model for Perceiving Social Context in Intelligent Environments.- Penguin Quart - Slovak Digit Speech Recognition Game Based on HMM.- Impact of Face Registration Errors on Recognition.- Unsupervised Segmentation of Meeting Configurations and Activities using Speech Activity Detection.- A Model of Real-Time Indoor Surveillance System using Behavior Detection.- A Filter Module Used in Pedestrian Detection System.- User Localization for Intelligent Crisis Management.- An Intrusion Detection System for Network-Initiated Attacks Using a Hybrid Neural Network.- Which Adequate Trust Model for Trust Networks?.- XML Systems for Intelligent Management of Pervasive Computing Resources.- A constraint based approach for aiding heat treatment operation design and distortion evaluation.- Applying AI to Cooperating Agricultural Robots.- Capacity Evaluation of an Indoor Smart Antenna System at 60 GHz.- Steady State Contingency analysis of electrical networks using machine learning techniques.- Robust Multimodal Audio-Visual Processing for Advanced Context Awareness in Smart Spaces.- Toward supporting group dynamics.- Multimodal Integration of Sensor Network.- Multimodal Identity Tracking in a Smartroom.- Multimodal Focus Attention and Stress Detection and feedback in an Augmented Driver Simulator.- A Fuzzy Expert System for the Early Warning of Accidents Due to Driver Hypo-Vigilance.- Mixed Reality Cane Simulation.- 3D content-based search using sketches.- Manual Annotation and Automatic Image Processing of Multimodal Emotional Behaviors in TV Interviews.- MPEG-4 Facial Expression Synthesis based on Appraisal Theory.- Towards On- and Off-line Search, Browse and Replay of Home Activities.- Engineering an interoperable adaptive hypermedia testing tool supporting user adaptable strategies.- Teaching a Computer Science Course using Adaptable Instructional Images.- e-Class Personalized: Design and Evaluation of an Adaptive Learning Content Management System.- The Use of Psychophysiological Measures for Designing Adaptive Learning Systems.- Developing Personalized E-Books: A Multi-Layered Approach.- Designing a Solver for Arithmetic Constraints to Support Education in Mathematics.- A Tutoring System Discovering Gaps in the Current Body of Students' Knowledge.- Sequencing Parametric Exercises for an Operating System Course.- A gene expression analysis system for medical diagnosis.- Recording, Monitoring and Interrelating Changes of Invivo Bio-cells from Video (Biosignatures).- An Archetype for MRI guided Tele-interventions.- Differential Evolution Algorithms for Finding Predictive Gene Subsets in Microarray Data.- Feature Selection for Microarray Data Analysis Using Mutual Information and Rough Set Theory.- A Support Vector Machine Approach to Breast Cancer Diagnosis and Prognosis.- Source Code Author Identification Based on N-gram Author Profiles.- AJA - Tool for Programming Adaptable Agents.- Investigating the Predictability of Empirical Software Failure Data with Artificial Neural Networks and Hybrid Models.- Selecting the Appropriate Machine Learning Techniques for the Prediction of Software Development Costs.- On the Idea of Using Nature-Inspired Metaphors to Improve Software Testing.- Fast Video Object Tracking using Affine Invariant Normalization.- Knowledge Acquisition from Multimedia Content using an Evolution Framework.- Exploratory Search: Image Retrieval without Deep Semantics.- Word Senses: The Stepping Stones in Semantic-Based Natural Language Processing.- Space-Time Tubes and Motion Representation.- Semantic Concept Detection from News Videos with Self-Organizing Maps.- Analysis of Semantic Information Available in an Image Collection Augmented with Auxiliary Data.- Supporting Semi-Automatic Semantic Annotation of Multimedia Resources.- A Simulation Tool for Modelling Pedestrian Dynamics during Evacuation of Large Areas.- Radar Imaging by Range Density Functions.- A Method for Incremental Data Fusion in Distributed Sensor Networks.- Information Society: the two faces of Janus.- Ant Seeker: An algorithm for enhanced web search.- Increasing Intelligent Wireless Sensor Networks Survivability by Applying Energy-Efficient Schemes.- A Review of Video Watermarking and a Benchmarking Framework.- Chaotic Encryption Driven Watermarking of Human Video Objects Based on Hu Moments.- Semi-Fragile Watermarking Authentication with Local and Global Watermarks.- Decentralising the Digital Rights Management Value Chain by means of Distributed License Catalogues.- AXMEDIS architectural solution for interoperable content and DRM on multichannel distribution.- Computer Aided Diagnosis of CT Focal Liver Lesions based on Texture Features, Feature Selection and Ensembles of Classifiers.- Texture Analysis for Classification of Endometrial Tissue in Gray Scale Transvaginal Ultrasonography.- Wavelet-based Feature Analysis for Classification of Breast Masses from Normal Dense Tissue.- Microcalcification Features Extracted from Principal Component Analysis in the Wavelet Domain.- Classification of Atherosclerotic Carotid Plaques Using Gray Level Morphological Analysis on Ultrasound images.
• (source: Nielsen Book Data)

Artificial Intelligence applications build on a rich and proven theoretical background to provide solutions to a wide range of real life problems. The ever expanding abundance of information and computing power enables researchers and users to tackle higly interesting issues for the first time, such as applications providing personalized access and interactivity to multimodal information based on preferences and semantic concepts or human-machine interface systems utilizing information on the affective state of the user. The purpose of the 3rd IFIP Conference on Artificial Intelligence Applications and Innovations (AIAI) is to bring together researchers, engineers, and practitioners interested in the technical advances and business and industrial applications of intelligent systems. AIAI 2006 is focused on providing insights on how AI can be implemented in real world applications.
(source: Nielsen Book Data)

• Knowledge and Information Management.- Can Common Sense uncover cultural differences in computer applications?.- Artificial Intelligence and Knowledge Management.- Agents 1.- The IONWI Algorithm: Learning when and when not to interrupt.- Formal Analysis of the Communication of Probabilistic Knowledge.- Detecting and Repairing Anomalous Evolutions in Noisy Environments: Logic Programming Formalization and Complexity Results.- Adding Semantic Web Services Matching and Discovery Support to the MoviLog Platform.- Learning Browsing Patterns for Context-Aware Recommendation.- Applying Collaborative Filtering to Reputation Domain: a model for more precise reputation estimates in case of changing behavior by rated participants.- Integration of AI with other Technologies.- Combine Vector Quantization and Support Vector Machine for Imbalanced Datasets.- Ontology Support for Translating Negotiation Primitives.- Statistical Method of Context Evaluation for Biological Sequence Similarity.- Biological inspired algorithm for Storage Area Networks (ACOSAN).- Neural Nets.- Radial Basis Functions Versus Geostatistics in Spatial Interpolations.- Neural Networks applied to wireless communications.- Anomaly Detection using prior knowledge: application to TCP/IP traffic.- A study on the ability of Support Vector Regression and Neural Networks to Forecast Basic Time Series Patterns.- Neural Plasma.- Knowledge Acquisition and Data Mining.- Comparison of SVM and Some Older Classification Algorithms in Text Classification Tasks.- An automatic graph layout procedure to visualize correlated data.- Knowledge Perspectives in Data Grids.- On the Class Distribution Labelling Step Sensitivity of CO-TRAINING.- Two new feature selection algorithms with Rough Sets Theory.- Evolutionary Computation.- Global Convexity in the Bi-Criteria Traveling Salesman Problem.- Evolutionary Algorithm for State Encoding.- Hypercube FrameWork for ACO applied to timetabling.- Multitree-Multiobjective Multicast Routing for Traffic Engineering.- Speech and Natural Language.- Road Segment Identification in Natural Language Text.- Learning Discourse-new References in Portuguese Texts.- Analysing Definition Questions by Two Machine Learning Approaches.- Fuzzy Rule-Based Hand Gesture Recognition.- Comparison of distance measures for historical spelling variants.- Industrial Applications of AI.- Patterns in Temporal Series of Meteorological Variables Using SOM & TDIDT.- Applying Genetic Algorithms to Convoy Scheduling.- A GRASP algorithm to solve the problem of dependent tasks scheduling in different machines.- A support vector machine as an estimator of mountain papaya ripeness using resonant frequency or frequency centroid.- FieldPlan: Tactical Field Force Planning in BT.- An Agent Solution to Flexible Planning and Scheduling of Passenger Trips.- Machine Vision.- Facial expression recognition using shape and texture information.- Limited Receptive Area neural classifier for texture recognition of metal surfaces.- A Tracking Framework for Accurate Face Localization.- A Combination of Spatiotemporal ICA and Euclidean Features for Face Recognition.- Agents 2.- Three Technologies for Automated Trading.- Modeling Travel Assistant Agents: a graded BDI approach.- e-Tools: An agent coordination layer to support the mobility of persons with disabilities..- Expert Systems.- Conceptualization Maturity Metrics for Expert Systems.- Toward developing a tele-diagnosis system on fish disease.- A new method for fish-disease diagnostic problem solving based on parsimonious covering theory and fuzzy inference model.- Effective Prover for Minimal Inconsistency Logic.- Identification of Important News for Exchange Rate Modeling.- Planning and Scheduling.- Autonomous Search and Rescue Rotorcraft Mission Stochastic Planning with Generic DBNs.- Solving multi-objective scheduling problems-An integrated systems approach.
• (source: Nielsen Book Data)

This volume presents proceedings from the 19th IFIP World Computer Congress in Santiago, Chile. The proceedings of the World Computer Congress are a product of the gathering of 2,000 delegates from more than 70 countries to discuss a myriad of topics in the ICT domain. Of particular note, this marks the first time that a World Computer Congress has been held in a Latin American country. Topics in this series include: the 4th International Conference on Theoretical Computer Science; Education for the 21st Century; Impact of ICT and Digital Resources; Mobile and Wireless Communication Networks; Ad-Hoc Networking; Network Control and Engineering for QoS, Security, and Mobility; The Past and Future of Information Systems: 1976-2006 and Beyond; History of Computing and Education; Biologically Inspired Cooperative Computing; Artificial Intelligence in Theory and Practice; Applications in Artificial Intelligence; and, Advanced Software Engineering: Expanding the Frontiers of Software.
(source: Nielsen Book Data)
The papers in this volume comprise the refereed proceedings of the conference 'Artificial Intelligence in Theory and Practice' (IFIP AI 2006), which formed part of the 19th World Computer Congress of IFIP, the International Federation for Information Processing (WCC- 2006), in Santiago, Chile in August 2006. The conference is organised by the IFIP Technical Committee on Artificial Intelligence (Technical Committee 12) and its Working Group 12.5 (Artificial Intelligence Applications). All papers were reviewed by at least two members of our Programme Committee. The best papers were selected for the conference and are included in this volume. The international nature of IFIP is amply reflected in the large number of countries represented here. The conference featured invited talks by Rose Dieng, John Atkinson, John Debenham and myself. IFIP AI 2006 also included the Second IFIP Symposium on Professional Practice in Artificial Intelligence, organised by Professor John Debenham, which ran alongside the refereed papers. I should like to thank the conference chair. Professor Debenham for all his efforts in organising the Symposium and the members of our programme committee for reviewing an unexpectedly large number of papers to a very tight deadline. This is the latest in a series of conferences organised by IFIP Technical Committee 12 dedicated to the techniques of Artificial Intelligence and their real-world applications. The wide range and importance of these applications is clearly indicated by the papers in this volume. Further information about TCI 2 can be found on our website http://www.ifiptcl2.org.
(source: Nielsen Book Data)

• What Is Computational Intelligence and Where Is It Going?.- New Millennium AI and the Convergence of History.- The Challenges of Building Computational Cognitive Architectures.- Programming a Parallel Computer: The Ersatz Brain Project.- The Human Brain as a Hierarchical Intelligent Control System.- Artificial Brain and OfficeMate TR based on Brain Information Processing Mechanism.- Natural Intelligence and Artificial Intelligence: Bridging the Gap between Neurons and Neuro-Imaging to Understand Intelligent Behaviour.- Computational Scene Analysis.- Brain-, Gene-, and Quantum Inspired Computational Intelligence: Challenges and Opportunities.- The Science of Pattern Recognition. Achievements and Perspectives.- Towards Comprehensive Foundations of Computational Intelligence.- Knowledge-Based Clustering in Computational Intelligence.- Generalization in Learning from Examples.- A Trend on Regularization and Model Selection in Statistical Learning: A Bayesian Ying Yang Learning Perspective.- Computational Intelligence in Mind Games.- Computer Go: A Grand Challenge to AI.- Noisy Chaotic Neural Networks for Combinatorial Optimization.
• (source: Nielsen Book Data)

In recent years computational intelligence has been extended by adding many other subdisciplines and this new field requires a series of challenging problems that will give it a sense of direction in order to ensure that research efforts are not wasted. This book written by top experts in computational intelligence provides such clear directions and a much-needed focus on the most important and challenging research issues.
(source: Nielsen Book Data)

• Plenary Talk.- From Search Engines to Question-Answering Systems: The Problems of World Knowledge, Relevance, Deduction, and Precisiation.- Invited Session: Fuzzy Multiperson and Multicriteria Decisions Modelling.- A Fuzzy Approach to Optimal R&D Project Portfolio Selection.- Choquet Integration and Correlation Matrices in Fuzzy Inference Systems.- Linguistic Summarization of Some Static and Dynamic Features of Consensus Reaching.- Consistency for Nonadditive Measures: Analytical and Algebraic Methods.- Neural Nets.- Neuro-Fuzzy Kolmogorov's Network with a Modified Perceptron Learning Rule for Classification Problems.- A Self-Tuning Controller for Teleoperation System using Evolutionary Learning Algorithms in Neural Networks.- A Neural-Based Method for Choosing Embedding Dimension in Chaotic Time Series Analysis.- On Classification of Some Hopfield-Type Learning Rules via Stability Measures.- Applications I.- A New Genetic Based Algorithm for Channel Assignment Problems.- Max-Product Fuzzy Relational Equations as Inference Engine for Prediction of Textile Yarn Properties.- Automatic Defects Classification and Feature Extraction Optimization.- Short-Term Load Forecasting in Power System Using Least Squares Support Vector Machine.- Plenary Talk.- Fifteen Years of Fuzzy Logic in Dortmund.- Invited Session: Intuitionistic Fuzzy Sets and Generalized Nets I.- Intuitionistic Fuzzy Graphs.- On Some Intuitionistic Properties of Intuitionistic Fuzzy Implications and Negations.- On Intuitionistic Fuzzy Negations.- Invited Session: Soft Computing Techniques for Reputation and Trust I.- A Simulation Model for Trust and Reputation System Evaluation in a P2P Network.- A Fuzzy Trust Model Proposal to Ensure the Identity of a User in Time.- Quantification of the Effectiveness of the Markov Model for Trustworthiness Prediction.- Applications II.- Fuzzy-Genetic Methodology for Web-based Computed-Aided Diagnosis in Medical Applications.- Weight Optimization for Loan Risk Estimation with Genetic Algorithm.- A Fuzzy Feature Extractor Neural Network and its Application in License Plate Recognition.- Invited Session: Intuitionistic Fuzzy Sets and Generalized Nets II.- Nearest Interval Approximation of an Intuitionistic Fuzzy Number.- On Intuitionistic Fuzzy Expert Systems With Temporal Parameters.- Generalized Fuzzy Cardinalities of IF Sets.- Invited Session: Soft Computing Techniques for Reputation and Trust II.- Towards Usage Policies for Fuzzy Inference Methodologies for Trust and QoS Assessment.- Simulating a Trust-Based Peer-to-Peer Metadata Publication Center.- The Complex Facets of Reputation and Trust.- Theory I.- Fuzzy Covering Relation and Ordering: An Abstract Approach.- Measures of Differentiability.- Lipschitz Continuity of Triangular Norms.- Plenary Talk.- Formal Models of Knowledge Operators: Rough-Set-Style and Fuzzy-Set-Style Approaches.- Invited Session: Looking at Language with Fuzzy Logic.- Using a Fuzzy Model for Combining Search Results from Different Information Sources to Build a Metasearch Engine.- Some Fuzzy Counterparts of the Language uses of And and Or.- Fuzzy Sets Versus Language.- Theory II.- Some Properties of Fuzzy Languages.- General Form of Lattice Valued Intuitionistic Fuzzy Sets.- A Note on Generated Pseudo-Operations with Two Parameters as a base for the Generalized Pseudo-Laplace Type Transform.- Theory III.- Fuzzy All-Pairs Shortest Paths Problem.- Optimal Toll Charges in a Fuzzy Flow Problem.- Modified Interval Global Weights in AHP.- Plenary Talk.- Fuzzy Approaches to Trust Management.- Invited Session: Complex-Valued Neural Networks.- Proposal of Holographic 3D-Movie Generation Using Coherent Neural-Network Interpolation.- Blur Identification Using Neural Network for Image Restoration.- Solving the Parity n Problem and Other Nonlinearly Separable Problems Using a Single Universal Binary Neuron.- Some Novel Real/Complex-Valued Neural Network Models.- Theory IV.- Extending the Fuzzy Rule Interpolation "FIVE" by Fuzzy Observation.- Fuzzy Rule Interpolation Based on Polar Cuts.- Approximate Reasoning Using Fodor's Implication.- Plenary Talk.- Brain-, Gene-, and Quantum-Inspired Computational Intelligence: Challenges and Opportunities.- Invited Session: Intelligent Data Mining.- Incremental Learning for E-mail Classification.- Reduction of Search Space for Instance-Based Classifier Combination.- Invited Session: Preferences and Decisions.- Linguistic Matrix Aggregation Operators: Extensions of the Borda Rule.- Evolutionary Algorithms.- An Evolutionary Algorithm for the Biobjective QAP.- On a Hill-Climbing Algorithm with Adaptive Step Size: Towards a Control Parameter-Less Black-Box Optimisation Algorithm.- Self-Adaptive Baldwinian Search in Hybrid Genetic Algorithms.- Intragenerational Mutation Shape Adaptation.- Theory V.- The Choquet-Integral as an Aggregation Operator in Case-Based Learning.- Fuzzy Sets and Multicriteria Decision Making.- Fuzzy Reinforcement Learning for Routing in Wireless Sensor Networks.- Outlier Resistant Recursive Fuzzy Clustering Algorithms.- Invited Session: Fuzzy Sets - 40 years after.- Fuzzy Set Theory - 40 Years of Foundational Discussions.- Fuzzy Control - Expectations, Current State, and Perspectives.- Fuzzy Sets in Categories of Sets with Similarity Relations.- Fuzzy Sets as a Special Mathematical Model of Vagueness Phenomenon.- Fuzzy IF-THEN Rules from Logical Point of View.- Applications III.- Synthesizing Adaptive Navigational Robot Behaviours Using a Hybrid Fuzzy A* Approach.- Fuzzy Impulse Noise Reduction Methods for Color Images.- Use of Variable Fuzzy Sets Methods for Desertification Evaluation.- A Fuzzy Ultrasonic System for Estimating Degradation of Insulating Oil.- A Genetic Algorithm-Based Fuzzy Inference System in Prediction of Wave Parameters.- Poster Contributions.- Estimation of Degree of Polymerisation and Residual Age of Transformers Based on Furfural Levels in Insulating Oil Through Generalized Regression Neural Networks.- Fuzzy Shortest Paths in Fuzzy Graphs.- Improving Vegas Algorithm Using PID and Fuzzy PID Controllers.- A Fuzzy-Based Automation Level Analysis in Irrigation Equipment.- Motorized Skateboard Stabilization Using Fuzzy Controller.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 9th Dortmund Fuzzy Days, Dortmund, Germany, 2006. This conference has established itself as an international forum for the discussion of new results in the field of Computational Intelligence. The papers presented here, all thoroughly reviewed, are devoted to foundational and practical issues in fuzzy systems, neural networks, evolutionary algorithms, and machine learning and thus cover the whole range of computational intelligence.
(source: Nielsen Book Data)

• E-Services and Intelligent Techniques.- E-Service Intelligence: An Introduction.- Rough Sets and Conflict Analysis.- Rough Ontology Mapping in E-Business Integration.- Concept-based Semantic Web Search and Q&A.- E-Service Composition Tools from a Lifecycle Perspective.- Web Information Presentation, Search, and Mining.- Web Information Representation, Extraction, and Reasoning based on Existing Programming Technology.- Techniques and Technologies Behind Maps of Internet and Intranet Document Collections.- Exchange Rate Modelling for E-Negotiators Using Text Mining Techniques.- A Similarity-aware Web Content Caching Scheme and Agentbased Web Document Pre-fetching.- Representation and Discovery of Intelligent E-Services.- Learning the Nonlinear Dynamics of Cyberlearning.- Personalization, Privacy, and Trust in E-Services.- Personalizing E-Commerce with Data Mining.- Personal eBanking Solutions based on Semantic Web Services.- Secure E-Transactions Protocol using Intelligent Mobile Agents with Fair Privacy.- Trust and Reputation in E- ervices: Concepts, Models and Applications.- An Incremental Technique for Analyzing User Behaviors in an E-Business Environment.- Customer Experience Management in E-Services.- E-Service Evaluation, Optimization, and Knowledge Discovery.- E-Service Cost Benefit Evaluation and Analysis.- Evaluation of Experience-based Support for Organizational Employees.- A Web based Intelligent Sensory Evaluation System in the Textile Integrated Supply Chain.- E-intelligence in Portfolio Investment Optimization.- Orchestrating the Knowledge Discovery Process.- On the Knowledge Repository Design and Management in E-Learning.- Adding Value to E-Services: a Business-Oriented Model.- Intelligent E-Service Support System Developments.- Developing a Knowledge-based Intelligent Services System in Sports Websites.- Developing a Model Agent-based E-Commerce System.- Creating Visual Browsers for Large-Scale Online Auctions.- Design and Implementation of Multi-Agents for Learner-oriented Course Scheduling on the Internet.- AGrIP - Agent Grid Intelligence Platform.- Web-based Service Information Systems based on Fuzzy Linguistic Techniques and Semantic Web Technologies.- Application of Chaos-based Pseudo-Random-Bit Generators in Internet-based Online Payments.- Computer Hardware Devices in Efficient E-Servicing: Case Study of Disk Scheduling by Soft Computing.
• (source: Nielsen Book Data)

Business organizations and governments are nowadays developing and providing internet based electronic services (e-services) featuring various intelligent functions. This book offers a thorough introduction and systematic overview of the new field e-service intelligence. It covers the state-of-the-art of e-service intelligence including both theorems and applications, and a broad range of topics are discussed.
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• A Bayesian Approach to Causal Discovery.- A Tutorial on Learning Causal Influence.- Learning Based Programming.- N-1 Experiments Suffice to Determine the Causal Relations Among N Variables.- Support Vector Inductive Logic Programming.- Neural Probabilistic Language Models.- Computational Grammatical Inference.- On Kernel Target Alignment.- The Structure of Version Space.
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Machine learning is currently one of the most rapidly growing areas of research in computer science. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. This book covers the three main learning systems; symbolic learning, neural networks and genetic algorithms as well as providing a tutorial on learning casual influences. Each of the nine chapters is self-contained. Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for Postgraduate since it shows the direction of current research.
(source: Nielsen Book Data)

This 1179-page book assembles the complete contributions to the International Conference on Intelligent Computing, ICIC 2006: one volume of Lecture Notes in Computer Science (LNCS); one of Lecture Notes in Artificial Intelligence (LNAI); one of Lecture Notes in Bioinformatics (LNBI); and two volumes of Lecture Notes in Control and Information Sciences (LNCIS). Include are 149 revised full papers, and a Special Session on Computing for Searching Strategies to Control Dynamic Processes.
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• Knowledge Representation.- Analyzing Parameter Sensitivity and Classifier Representations for Real-Valued XCS.- Use of Learning Classifier System for Inferring Natural Language Grammar.- Backpropagation in Accuracy-Based Neural Learning Classifier Systems.- Binary Rule Encoding Schemes: A Study Using the Compact Classifier System.- Mechanisms.- Bloat Control and Generalization Pressure Using the Minimum Description Length Principle for a Pittsburgh Approach Learning Classifier System.- Post-processing Clustering to Decrease Variability in XCS Induced Rulesets.- LCSE: Learning Classifier System Ensemble for Incremental Medical Instances.- Effect of Pure Error-Based Fitness in XCS.- A Fuzzy System to Control Exploration Rate in XCS.- Counter Example for Q-Bucket-Brigade Under Prediction Problem.- An Experimental Comparison Between ATNoSFERES and ACS.- The Class Imbalance Problem in UCS Classifier System: A Preliminary Study.- Three Methods for Covering Missing Input Data in XCS.- New Directions.- A Hyper-Heuristic Framework with XCS: Learning to Create Novel Problem-Solving Algorithms Constructed from Simpler Algorithmic Ingredients.- Adaptive Value Function Approximations in Classifier Systems.- Three Architectures for Continuous Action.- A Formal Relationship Between Ant Colony Optimizers and Classifier Systems.- Detection of Sentinel Predictor-Class Associations with XCS: A Sensitivity Analysis.- Application-Oriented Research and Tools.- Data Mining in Learning Classifier Systems: Comparing XCS with GAssist.- Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule.- Using XCS to Describe Continuous-Valued Problem Spaces.- The EpiXCS Workbench: A Tool for Experimentation and Visualization.
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This book constitutes the thoroughly refereed joint post-proceedings of three consecutive International Workshops on Learning Classifier Systems that took place in Chicago, IL in July 2003, in Seattle, WA in June 2004, and in Washington, DC in June 2005. Topics in the 22 revised full papers range from theoretical analysis of mechanisms to practical consideration for successful application of such techniques to everyday datamining tasks.
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• Invited Presentations.- Property Testing: A Learning Theory Perspective.- Spectral Algorithms for Learning and Clustering.- Unsupervised, Semisupervised and Active Learning I.- Minimax Bounds for Active Learning.- Stability of k-Means Clustering.- Margin Based Active Learning.- Unsupervised, Semisupervised and Active Learning II.- Learning Large-Alphabet and Analog Circuits with Value Injection Queries.- Teaching Dimension and the Complexity of Active Learning.- Multi-view Regression Via Canonical Correlation Analysis.- Statistical Learning Theory.- Aggregation by Exponential Weighting and Sharp Oracle Inequalities.- Occam's Hammer.- Resampling-Based Confidence Regions and Multiple Tests for a Correlated Random Vector.- Suboptimality of Penalized Empirical Risk Minimization in Classification.- Transductive Rademacher Complexity and Its Applications.- Inductive Inference.- U-Shaped, Iterative, and Iterative-with-Counter Learning.- Mind Change Optimal Learning of Bayes Net Structure.- Learning Correction Grammars.- Mitotic Classes.- Online and Reinforcement Learning I.- Regret to the Best vs. Regret to the Average.- Strategies for Prediction Under Imperfect Monitoring.- Bounded Parameter Markov Decision Processes with Average Reward Criterion.- Online and Reinforcement Learning II.- On-Line Estimation with the Multivariate Gaussian Distribution.- Generalised Entropy and Asymptotic Complexities of Languages.- Q-Learning with Linear Function Approximation.- Regularized Learning, Kernel Methods, SVM.- How Good Is a Kernel When Used as a Similarity Measure?.- Gaps in Support Vector Optimization.- Learning Languages with Rational Kernels.- Generalized SMO-Style Decomposition Algorithms.- Learning Algorithms and Limitations on Learning.- Learning Nested Halfspaces and Uphill Decision Trees.- An Efficient Re-scaled Perceptron Algorithm for Conic Systems.- A Lower Bound for Agnostically Learning Disjunctions.- Sketching Information Divergences.- Competing with Stationary Prediction Strategies.- Online and Reinforcement Learning III.- Improved Rates for the Stochastic Continuum-Armed Bandit Problem.- Learning Permutations with Exponential Weights.- Online and Reinforcement Learning IV.- Multitask Learning with Expert Advice.- Online Learning with Prior Knowledge.- Dimensionality Reduction.- Nonlinear Estimators and Tail Bounds for Dimension Reduction in l 1 Using Cauchy Random Projections.- Sparse Density Estimation with ?1 Penalties.- ?1 Regularization in Infinite Dimensional Feature Spaces.- Prediction by Categorical Features: Generalization Properties and Application to Feature Ranking.- Other Approaches.- Observational Learning in Random Networks.- The Loss Rank Principle for Model Selection.- Robust Reductions from Ranking to Classification.- Open Problems.- Rademacher Margin Complexity.- Open Problems in Efficient Semi-supervised PAC Learning.- Resource-Bounded Information Gathering for Correlation Clustering.- Are There Local Maxima in the Infinite-Sample Likelihood of Gaussian Mixture Estimation?.- When Is There a Free Matrix Lunch?.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 20th Annual Conference on Learning Theory, COLT 2007, held in San Diego, CA, USA in June 2007. It covers unsupervised, semisupervised and active learning, statistical learning theory, inductive inference, regularized learning, kernel methods, SVM, online and reinforcement learning, learning algorithms and limitations on learning, dimensionality reduction, as well as open problems.
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• Multi-Objective Clustering, Feature Extraction and Feature Selection.- Feature Selection Using Rough Sets.- Multi-Objective Clustering and Cluster Validation.- Feature Selection for Ensembles Using the Multi-Objective Optimization Approach.- Feature Extraction Using Multi-Objective Genetic Programming.- Multi-Objective Learning for Accuracy Improvement.- Regression Error Characteristic Optimisation of Non-Linear Models.- Regularization for Parameter Identification Using Multi-Objective Optimization.- Multi-Objective Algorithms for Neural Networks Learning.- Generating Support Vector Machines Using Multi-Objective Optimization and Goal Programming.- Multi-Objective Optimization of Support Vector Machines.- Multi-Objective Evolutionary Algorithm for Radial Basis Function Neural Network Design.- Minimizing Structural Risk on Decision Tree Classification.- Multi-objective Learning Classifier Systems.- Multi-Objective Learning for Interpretability Improvement.- Simultaneous Generation of Accurate and Interpretable Neural Network Classifiers.- GA-Based Pareto Optimization for Rule Extraction from Neural Networks.- Agent Based Multi-Objective Approach to Generating Interpretable Fuzzy Systems.- Multi-objective Evolutionary Algorithm for Temporal Linguistic Rule Extraction.- Multiple Objective Learning for Constructing Interpretable Takagi-Sugeno Fuzzy Model.- Multi-Objective Ensemble Generation.- Pareto-Optimal Approaches to Neuro-Ensemble Learning.- Trade-Off Between Diversity and Accuracy in Ensemble Generation.- Cooperative Coevolution of Neural Networks and Ensembles of Neural Networks.- Multi-Objective Structure Selection for RBF Networks and Its Application to Nonlinear System Identification.- Fuzzy Ensemble Design through Multi-Objective Fuzzy Rule Selection.- Applications of Multi-Objective Machine Learning.- Multi-Objective Optimisation for Receiver Operating Characteristic Analysis.- Multi-Objective Design of Neuro-Fuzzy Controllers for Robot Behavior Coordination.- Fuzzy Tuning for the Docking Maneuver Controller of an Automated Guided Vehicle.- A Multi-Objective Genetic Algorithm for Learning Linguistic Persistent Queries in Text Retrieval Environments.- Multi-Objective Neural Network Optimization for Visual Object Detection.
• (source: Nielsen Book Data)

Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This monograph presents a selected collection of research work on multi-objective approach to machine learning, including multi-objective feature selection, multi-objective model selection in training multi-layer perceptrons, radial-basis-function networks, support vector machines, decision trees, and intelligent systems.
(source: Nielsen Book Data)

• Kernel-Based Fusion.- Combining Pattern Recognition Modalities at the Sensor Level Via Kernel Fusion.- The Neutral Point Method for Kernel-Based Combination of Disjoint Training Data in Multi-modal Pattern Recognition.- Kernel Combination Versus Classifier Combination.- Deriving the Kernel from Training Data.- Applications.- On the Application of SVM-Ensembles Based on Adapted Random Subspace Sampling for Automatic Classification of NMR Data.- A New HMM-Based Ensemble Generation Method for Numeral Recognition.- Classifiers Fusion in Recognition of Wheat Varieties.- Multiple Classifier Methods for Offline Handwritten Text Line Recognition.- Applying Data Fusion Methods to Passage Retrieval in QAS.- A Co-training Approach for Time Series Prediction with Missing Data.- An Improved Random Subspace Method and Its Application to EEG Signal Classification.- Ensemble Learning Methods for Classifying EEG Signals.- Confidence Based Gating of Colour Features for Face Authentication.- View-Based Eigenspaces with Mixture of Experts for View-Independent Face Recognition.- Fusion of Support Vector Classifiers for Parallel Gabor Methods Applied to Face Verification.- Serial Fusion of Fingerprint and Face Matchers.- Boosting.- Boosting Lite - Handling Larger Datasets and Slower Base Classifiers.- Information Theoretic Combination of Classifiers with Application to AdaBoost.- Interactive Boosting for Image Classification.- Cluster and Graph Ensembles.- Group-Induced Vector Spaces.- Selecting Diversifying Heuristics for Cluster Ensembles.- Unsupervised Texture Segmentation Using Multiple Segmenters Strategy.- Classifier Ensembles for Vector Space Embedding of Graphs.- Cascading for Nominal Data.- Feature Subspace Ensembles.- A Combination of Sample Subsets and Feature Subsets in One-Against-Other Classifiers.- Random Feature Subset Selection for Ensemble Based Classification of Data with Missing Features.- Feature Subspace Ensembles: A Parallel Classifier Combination Scheme Using Feature Selection.- Stopping Criteria for Ensemble-Based Feature Selection.- Multiple Classifier System Theory.- On Rejecting Unreliably Classified Patterns.- Bayesian Analysis of Linear Combiners.- Applying Pairwise Fusion Matrix on Fusion Functions for Classifier Combination.- Modelling Multiple-Classifier Relationships Using Bayesian Belief Networks.- Classifier Combining Rules Under Independence Assumptions.- Embedding Reject Option in ECOC Through LDPC Codes.- Intramodal and Multimodal Fusion of Biometric Experts.- On Combination of Face Authentication Experts by a Mixture of Quality Dependent Fusion Classifiers.- Index Driven Combination of Multiple Biometric Experts for AUC Maximisation.- Q???stack: Uni- and Multimodal Classifier Stacking with Quality Measures.- Reliability-Based Voting Schemes Using Modality-Independent Features in Multi-classifier Biometric Authentication.- Optimal Classifier Combination Rules for Verification and Identification Systems.- Majority Voting.- Exploiting Diversity in Ensembles: Improving the Performance on Unbalanced Datasets.- On the Diversity-Performance Relationship for Majority Voting in Classifier Ensembles.- Hierarchical Behavior Knowledge Space.- Ensemble Learning.- A New Dynamic Ensemble Selection Method for Numeral Recognition.- Ensemble Learning in Linearly Combined Classifiers Via Negative Correlation.- Naive Bayes Ensembles with a Random Oracle.- An Experimental Study on Rotation Forest Ensembles.- Cooperative Coevolutionary Ensemble Learning.- Robust Inference in Bayesian Networks with Application to Gene Expression Temporal Data.- An Ensemble Approach for Incremental Learning in Nonstationary Environments.- Invited Papers.- Multiple Classifier Systems in Remote Sensing: From Basics to Recent Developments.- Biometric Person Authentication Is a Multiple Classifier Problem.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 7th International Workshop on Multiple Classifier Systems, MCS 2007, held in Prague, Czech Republic in May 2007. It covers kernel-based fusion, applications, boosting, cluster and graph ensembles, feature subspace ensembles, multiple classifier system theory, intramodal and multimodal fusion of biometric experts, majority voting, and ensemble learning.
(source: Nielsen Book Data)

• Keynotes
• Text processing
• [Special] Fuzzy system applications
• Vision
• [Special] Real world interaction
• Genetic algorithm
• Robot
• Poster [session]
• Fuzzy logic
• Manufacturing
• Data mining
• Neural network
• Constraint satisfaction
• Machine learning
• [Special] Chance discovery and social network
• Education
• Speech
• [Special] E-commerce
• Heuristic search
• Application system
• Agent-based system
• Other applications.

This book constitutes the refereed proceedings of the 20th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2007, held in Kyoto, Japan. Coverage includes text processing, fuzzy system applications, real-world interaction, data mining, machine learning chance discovery and social networks, e-commerce, heuristic search application systems, and other applications.
(source: Nielsen Book Data)

• Learning and Neural Nets.- Detection of Breast Lesions in Medical Digital Imaging Using Neural Networks.- Identification of Velocity Variations in a Seismic Cube Using Neural Networks.- Improving the k-NN method: Rough Set in edit training set.- Agents.- Membranes as Multi-agent Systems: an Application to Dialogue Modelling.- Agent Planning, Models, Virtual Haptie Computing, and Visual Ontology.- Improving Interoperability Among Learning Objects Using FIPA Agent Communication Framework.- An Agent-Oriented Programming Language for Computing in Context.- Search.- A Little Respect (for the Role of Common Components in Heuristic Search).- Recursive and Iterative Algorithms for N-ary Search Problems.- Ontologies and Intelligent Web.- Process of Ontology Construction for the Development of an Intelligent System for the Organization and Retrieval of Knowledge in Biodiversity - SISBIO.- Service interdependencies: insight into use cases for service composition.- Combining Contexts and Ontologies: A Case Study.- The RR Project - A Framework for Relationship Network Viewing and Management.- Web Service-based Business Process Automation Using Matching Algorithms.- A Graphic Tool for Ontology Viewing Based on Graph Theory.- Knowledge Engineering.- Using Competence Modeling to create Knowledge Engineering Team.- Intelligent Systems Engineering with Reconfigurable Computing.- Ontological Evaluation in the Knowledge Based System.- A Model for Concepts Extraction and Context Identification in Knowledge Based Systems.- Proposal of Fuzzy Object Oriented Model in Extended JAVA.- Knowledge Discovery.- Adopting Knowledge Discovery in Databases for Customer Relationship Management in Egyptian Public Banks.- Pattern Discovery and Model Construction: an Evolutionary Learning and Data Mining Approach.- Towards a Framework for Knowledge Discovery.- Language Processing.- Prototype Of Speech Translation System For Audio Effective Communication.- A Knowledge Representation Semantic Network for a Natural Language Syntactic Analyzer Based on the UML.- Applications.- Fast simulation of animal locomotion: lamprey swimming.- Towards a case-based reasoning approach to analyze road accidents.- Solving the Short Run Economic Dispatch Problem Using Concurrent Constraint Programming.- Micromechanics as a Testbed for Artificial Intelligence Methods Evaluation.- Burst Synchronization in Two Pulse-Coupled Resonate-and-Fire Neuron Circuits.- Ant Colonies using Arc Consistency Techniques for the Set Partitioning Problem.- Automatic Query Recommendation using Click-Through Data.- A Shared-Memory Multiprocessor Scheduling Algorithm.- Web Attack Detection Using ID3.- A Statistical Sampling Strategy for Iris Recognition.- An Application of ARX Stochastic Models to Iris Recognition.
• (source: Nielsen Book Data)

The Second Symposium on Professional Practice in AI 2006 is a conference within the IFIP World Computer Congress 2006, Santiago, Chile. The Symposium is organised by the IFIP Technical Committee on Artificial Intelligence (Technical Committee 12) and its Working Group 12.5 (Artificial Intelligence Applications). The First Symposium in this series was one of the conferences in the IFIP World Computer Congi-ess 2004, Toulouse France. The conference featured invited talks by Rose Dieng, John Atkinson, John Debenham and Max Bramer. The Symposium was a component of the IFIP AI 2006 conference, organised by Professor Max Bramer. I should like to thank the Symposium General Chair, Professor Bramer for his considerable assistance in making the Symposium happen within a very tight deadline. These proceedings are the result of a considerable amount of hard work. Beginning with the preparation of the submitted papers, the papers were each reviewed by at least two members of the international Program Committee. The authors of accepted papers then revised their manuscripts to produce their final copy. The hard work of the authors, the referees and the Program Committee is gratefully aclaiowledged. The IFIP AI 2006 conference and the Symposium are the latest in a series of conferences organised by IFIP Technical Committee 12 dedicated to the techniques of Aitificial Intelligence and their real-world applications. Further infoirmation about TC12 can be found on our website http; //www.ifiptcI2.org.
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• Preface Acknowledgment PART I. Theoretical Foundation
• Chapter 1. The Goal of Artificial Intelligence 1.1 To define intelligence 1.2 Various schools in AI research 1.3 AI as a whole
• Chapter 2. A New Approach Toward AI 2.1 To define AI 2.2 Intelligent reasoning systems 2.3 Major design issues of NARS PART II. Non-Axiomatic Reasoning System
• Chapter 3. The Core Logic 3.1 NAL-0: binary inheritance 3.2 The language of NAL-1 3.3 The inference rules of NAL-1
• Chapter 4. First-Order Inference 4.1 Compound terms 4.2 NAL-2: sets and variants of inheritance 4.3 NAL-3: intersections and differences 4.4 NAL-4: products, images, and ordinary relations
• Chapter 5. Higher-Order Inference 5.1 NAL-5: statements as terms 5.2 NAL-6: statements with variables 5.3 NAL-7: temporal statements 5.4 NAL-8: procedural statements
• Chapter 6. Inference Control 6.1 Task management 6.2 Memory structure 6.3 Inference processes 6.4 Budget assessment . PART III. Comparison and Discussion
• Chapter 7. Semantics 7.1 Experience vs. model 7.2 Extension and intension 7.3 Meaning of term 7.4 Truth of statement
• Chapter 8. Uncertainty 8.1 The non-numerical approaches 8.2 The fuzzy approach 8.3 The Bayesian approach 8.4 Other probabilistic approaches 8.5 Unified representation of uncertainty
• Chapter 9. Inference Rules 9.1 Deduction 9.2 Induction 9.3 Abduction 9.4 Implication
• Chapter 10. NAL as a Logic 10.1 NAL as a term logic 10.2 NAL vs. predicate logic 10.3 Logic and AI
• Chapter 11. Categorization and Learning 11.1 Concept and categorization 11.2 Learning in NARS
• Chapter 12. Control and Computation 12.1 NARS and theoretical computer science 12.2 Various assumptions about resources 12.3 Dynamic natures of NARS PART IV. Conclusions
• Chapter 13. Current Results 13.1 Theoretical foundation 13.2 Formal model 13.3 Computer implementation
• Chapter 14. NARS in the Future 14.1 Next steps of the project 14.2 What NARS is not 14.3 General implications Bibliography Index.
• (source: Nielsen Book Data)

This book is the most comprehensive description of the decades-long Non-Axiomatic Reasoning System (NARS) project, including its philosophical foundation, methodological consideration, conceptual design details, implications in the related fields, and its similarities and differences to many related works in cognitive science. While most current works in Artificial Intelligence (AI) focus on individual aspects of intelligence and cognition, NARS is designed and developed to attack the AI problem as a whole.
(source: Nielsen Book Data)

• Prerequisites.- Simple Learning Classifier Systems.- The XCS Classifier System.- How XCS Works: Ensuring Effective Evolutionary Pressures.- When XCS Works: Towards Computational Complexity.- Effective XCS Search: Building Block Processing.- XCS in Binary Classification Problems.- XCS in Multi-Valued Problems.- XCS in Reinforcement Learning Problems.- Facetwise LCS Design.- Towards Cognitive Learning Classifier Systems.- Summary and Conclusions.
• (source: Nielsen Book Data)

Rule-basedevolutionaryonlinelearningsystems, oftenreferredtoasMichig- style learning classi?er systems (LCSs), were proposed nearly thirty years ago (Holland, 1976; Holland, 1977) originally calling them cognitive systems. LCSs combine the strength of reinforcement learning with the generali- tion capabilities of genetic algorithms promising a ?exible, online general- ing, solely reinforcement dependent learning system. However, despite several initial successful applications of LCSs and their interesting relations with a- mal learning and cognition, understanding of the systems remained somewhat obscured. Questions concerning learning complexity or convergence remained unanswered. Performance in di?erent problem types, problem structures, c- ceptspaces, andhypothesisspacesstayednearlyunpredictable. Thisbookhas the following three major objectives: (1) to establish a facetwise theory - proachforLCSsthatpromotessystemanalysis, understanding, anddesign; (2) to analyze, evaluate, and enhance the XCS classi?er system (Wilson, 1995) by the means of the facetwise approach establishing a fundamental XCS learning theory; (3) to identify both the major advantages of an LCS-based learning approach as well as the most promising potential application areas. Achieving these three objectives leads to a rigorous understanding of LCS functioning that enables the successful application of LCSs to diverse problem types and problem domains. The quantitative analysis of XCS shows that the inter- tive, evolutionary-based online learning mechanism works machine learning competitively yielding a low-order polynomial learning complexity. Moreover, the facetwise analysis approach facilitates the successful design of more - vanced LCSs including Holland's originally envisioned cognitive systems. Martin V.
(source: Nielsen Book Data)

• Methodologies Based on Particle Swarm Intelligence.- Swarm Intelligence: Foundations, Perspectives and Applications.- Waves of Swarm Particles (WoSP).- Grammatical Swarm: A Variable-Length Particle Swarm Algorithm.- SWARMs of Self-Organizing Polymorphic Agents.- Experiences Using Particle Swarm Intelligence.- Swarm Intelligence - Searchers, Cleaners and Hunters.- Ant Colony Optimisation for Fast Modular Exponentiation using the Sliding Window Method.- Particle Swarm for Fuzzy Models Identification.- A Matlab Implementation of Swarm Intelligence based Methodology for Identification of Optimized Fuzzy Models.
• (source: Nielsen Book Data)

Systems designers have learned that many agents co-operating within the system can solve very complex problems with a minimal design effort. In general, multi-agent systems that use swarm intelligence are said to be swarm intelligent systems. Today, these are mostly used as search engines and optimization tools. This volume reviews innovative methodologies of swarm intelligence, outlines the foundations of engineering swarm intelligent systems and applications, and relates experiences using the particle swarm optimisation.
(source: Nielsen Book Data)