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• Automating Inquiry Uncertainty Sampling Searching Through the Hypothesis Space Minimizing Expected Error and Variance Exploiting Structure in Data Theory Practical Considerations.
• (source: Nielsen Book Data)

The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose ""queries, "" usually in the form of unlabeled data instances to be labeled by an ""oracle"" (e.g., a human annotator) that already understands the nature of the problem. This sort of approach is well-motivated in many modern machine learning and data mining applications, where unlabeled data may be abundant or easy to come by, but training labels are difficult, time-consuming, or expensive to obtain. This book is a general introduction to active learning. It outlines several scenarios in which queries might be formulated, and details many query selection algorithms which have been organized into four broad categories, or ""query selection frameworks."" We also touch on some of the theoretical foundations of active learning, and conclude with an overview of the strengths and weaknesses of these approaches in practice, including a summary of ongoing work to address these open challenges and opportunities.
(source: Nielsen Book Data)

• Machine generated contents note: ch. 1 Introduction
• 1.1. Brief History of AI
• 1.2. Cognitive Issues of AI
• 1.3. Hierarchical Model of Thought
• 1.4. Symbolic Intelligence
• 1.5. Research Approaches of Artificial Intelligence
• 1.6. Automated Reasoning
• 1.7. Machine Learning
• 1.8. Distributed Artificial Intelligence
• 1.9. Artificial Thought Model
• 1.10. Knowledge Based Systems
• Exercises
• ch. 2 Logic Foundation of Artificial Intelligence
• 2.1. Introduction
• 2.2. Logic Programming
• 2.3. Nonmonotonic Logic
• 2.4. Closed World Assumption
• 2.5. Default Logic
• 2.6. Circumscription Logic
• 2.7. Nonmonotonic Logic NML
• 2.8. Autoepistemic Logic
• 2.9. Truth Maintenance System
• 2.10. Situation Calculus
• 2.11. Frame Problem
• 2.12. Dynamic Description Logic
• Exercises
• ch. 3 Constraint Reasoning
• 3.1. Introduction
• 3.2. Backtracking
• 3.3. Constraint Propagation
• 3.4. Constraint Propagation in Tree Search
• 3.5. Intelligent Backtracking and Truth Maintenance.

• 3.6. Variable Instantiation Ordering and Assignment Ordering
• 3.7. Local Revision Search
• 3.8. Graph-based Backjumping
• 3.9. Influence-based Backjumping
• 3.10. Constraint Relation Processing
• 3.11. Constraint Reasoning System COPS
• 3.12. ILOG Solver
• Exercise
• ch. 4 Qualitative Reasoning
• 4.1. Introduction
• 4.2. Basic approaches in qualitative reasoning
• 4.3. Qualitative Model
• 4.4. Qualitative Process
• 4.5. Qualitative Simulation Reasoning
• 4.6. Algebra Approach
• 4.7. Spatial Geometric Qualitative Reasoning
• Exercises
• ch. 5 Case-Based Reasoning
• 5.1. Overview
• 5.2. Basic Notations
• 5.3. Process Model
• 5.4. Case Representation
• 5.5. Case Indexing
• 5.6. Case Retrieval
• 5.7. Similarity Relations in CBR
• 5.8. Case Reuse
• 5.9. Case Retainion
• 5.10. Instance-Based Learning
• 5.11. Forecast System for Central Fishing Ground
• Exercises
• ch. 6 Probabilistic Reasoning
• 6.1. Introduction
• 6.2. Foundation of Bayesian Probability
• 6.3. Bayesian Problem Solving
• 6.4. Naive Bayesian Learning Model.

• 6.5. Construction of Bayesian Network
• 6.6. Bayesian Latent Semantic Model
• 6.7. Semi-supervised Text Mining Algorithms
• Exercises
• ch. 7 Inductive Learning
• 7.1. Introduction
• 7.2. Logic Foundation of Inductive Learning
• 7.3. Inductive Bias
• 7.4. Version Space
• 7.5. AQ Algorithm for Inductive Learning
• 7.6. Constructing Decision Trees
• 7.7. ID3 Learning Algorithm
• 7.8. Bias Shift Based Decision Tree Algorithm
• 7.9. Computational Theories of Inductive Learning
• Exercises
• ch. 8 Support Vector Machine
• 8.1. Statistical Learning Problem
• 8.2. Consistency of Learning Processes
• 8.3. Structural Risk Minimization Inductive Principle
• 8.4. Support Vector Machine
• 8.5. Kernel Function
• Exercises
• ch. 9 Explanation-Based Learning
• 9.1. Introduction
• 9.2. Model for EBL
• 9.3. Explanation-Based Generalization
• 9.4. Explanation Generalization using Global Substitutions
• 9.5. Explanation-Based Specialization
• 9.6. Logic Program of Explanation-Based Generalization
• 9.7. SOAR Based on Memory Chunks.

• 9.8. Operationalization
• 9.9. EBL with imperfect domain theory
• Exercises
• ch. 10 Reinforcement Learning
• 10.1. Introduction
• 10.2. Reinforcement Learning Model
• 10.3. Dynamic Programming
• 10.4. Monte Carlo Methods
• 10.5. Temporal-Difference Learning
• 10.6. Q-Learning
• 10.7. Function Approximation
• 10.8. Reinforcement Learning Applications
• Exercises
• ch. 11 Rough Set
• 11.1. Introduction
• 11.2. Reduction of Knowledge
• 11.3. Decision Logic
• 11.4. Reduction of Decision Tables
• 11.5. Extended Model of Rough Sets
• 11.6. Experimental Systems of Rough Sets
• 11.7. Granular Computing
• 11.8. Future Trends of Rough Set Theory
• Exercises
• ch. 12 Association Rules
• 12.1. Introduction
• 12.2. The Apriori Algorithm
• 12.3. FP-Growth Algorithm
• 12.4. CFP-Tree Algorithm
• 12.5. Mining General Fuzzy Association Rules
• 12.6. Distributed Mining Algorithm For Association Rules
• 12.7. Parallel Mining of Association Rules
• Exercises
• ch. 13 Evolutionary Computation
• 13.1. Introduction
• 13.2. Formal Model of Evolution System Theory.

• 13.3. Darwin's Evolutionary Algorithm
• 13.4. Classifier System
• 13.5. Bucket Brigade Algorithm
• 13.6. Genetic Algorithm
• 13.7. Parallel Genetic Algorithm
• 13.8. Classifier System Boole
• 13.9. Rule Discovery System
• 13.10. Evolutionary Strategy
• 13.11. Evolutionary Programming
• Exercises
• ch. 14 Distributed Intelligence
• 14.1. Introduction
• 14.2. The Essence of Agent
• 14.3. Agent Architecture
• 14.4. Agent Communication Language ACL
• 14.5. Coordination and Cooperation
• 14.6. Mobile Agent
• 14.7. Multi-Agent Environment MAGE
• 14.8. Agent Grid Intelligence Platform
• Exercises
• ch. 15 Artificial Life
• 15.1. Introduction
• 15.2. Exploration of Artificial Life
• 15.3. Artificial Life Model
• 15.4. Research Approach of Artificial Life
• 15.5. Cellular Automata
• 15.6. Morphogenesis Theory
• 15.7. Chaos Theories
• 15.8. Experimental Systems of Artificial Life
• Exercises.

Artificial intelligence is a branch of computer science and a discipline in the study of machine intelligence, that is, developing intelligent machines or intelligent systems imitating, extending and augmenting human intelligence through artificial means and techniques to realize intelligent behavior.Advanced Artificial Intelligence consists of 16 chapters. The content of the book is novel, reflects the research updates in this field, and especially summarizes the author's scientific efforts over many years. The book discusses the methods and key technology from theory, algorithm, system and applications related to artificial intelligence. This book can be regarded as a textbook for senior students or graduate students in the information field and related tertiary specialities. It is also suitable as a reference book for relevant scientific and technical personnel.
(source: Nielsen Book Data)

• Neural Networks.- Complex Functional Network Hebbian-Type Learning Algorithm and Convergence.- A New Intelligent Control Strategy of High-Voltage Power Supply for ECRH Based on CMAC Neural Network.- Self-configuration Using Artificial Neural Networks.- Evolutionary Learning and Genetic Algorithms.- An Improvement of AdaBoost for Face Detection with Random Forests.- Hybrid Good Point Set Evolutionary Strategy for Constrained Optimization.- Research of Modified Quantum Genetic Algorithm and It's Application in Collision Detection.- Granular Computing and Rough Sets.- A New Method of Attribute Reduction and Prediction in Fuzzy Decision System.- Particle Swarm Optimization and Niche Technology.- Development of Automatic Code Generation Tool for Condensation Algorithm.- Swarm Intelligence and Optimization.- A New Hybrid Multi-objective Pareto Archive PSO Algorithm for a Classic Job Shop Scheduling Problem with Ready Times.- Multi-objective Particle Swarm Optimization for Sequencing and Scheduling a Cellular Manufacturing System.- A Hybrid PSO Algorithm with Transposon for Multiobjective Optimization.- Independent Component Analysis and Blind Source Separation.- Joint Multichannel Blind Speech Separation and Dereverberation: A Real-Time Algorithmic Implementation.- An Efficient Pairwise Kurtosis Optimization Algorithm for Independent Component Analysis.- Combinatorial and Numerical Optimization.- The Mechanical Behavior of the Double Piece of Tape Spring.- Systems Biology and Computational Biology.- Cancer Immunoprevention: What Can We Learn from in Silico Models?.- Oscillatory Dynamics of Double Negative Feedback Loop Motif by MicroRNAs.- Neural Computing and Optimization.- Neural Network Approach for Greenery Warranty Systems.- Knowledge Discovery and Data Mining.- Comprehensive Evaluation of Effects of Naomaitong and Rhubarb Aglycone Combined with Bone Mesenchymal Stem Cells Transplantation on Brain in Rats with Cerebral Ischemia Based on Analytic Hierarchy Process.- Aggregating and Weighting Expert Knowledge in Group Decision Making.- A New Heuristic Feature Selection Algorithm Based on Rough Sets.- Developing a Procedure to Obtain Knowledge of Optimum Solutions in a Travelling Salesman Problem.- Trajectory Simplification and Classification for Moving Object with Road-Constraint.- Training a Pac-Man Player with Minimum Domain Knowledge and Basic Rationality.- Hybrid Self-Organizing Map and Neural Network Clustering Analysis for Technology Professionals Turnover Rate Forecasting.- Ensemble Methods.- A Study of Strength and Correlation in Random Forests.- Machine Learning Theory and Methods.- A Comparison Study of Conditional Random Fields Toolkits.- Robot Reinforcement Learning Based on Learning Classifier System.- Intelligent Computing in Bioinformatics.- Research of Marker Gene Selection for Tumor Classfication Based on Decision Forests.- Reverse Engineered Gene Networks Reveal Markers Predicting the Outcome of Breast Cancer.- New Tools for Expression Alternative Splicing Validation.- Intelligent Computing in Computational Biology and Drug Design.- Application for Artificial Bee Colony Algorithm in Migration of Mobile Agent.- Computational Genomics and Proteomics.- A Novel Tool for Assisted In-silico Cloning and Sequence Editing in Molecular Biology.- Intelligent Computing in Signal Processing.- A New Method Using Table to Sparse Representation in Pairs of Bases with Matching Pursuits.- Intelligent Computing in Pattern Recognition.- Palmprint Recognition Method Using WTA-ICA Based on 2DPCA.- Study of TCM Diagnosis of Syndromes of Acute Exacerbation of Chronic Obstructive Pulmonary Disease Based on Dynamic Fuzzy Kohonen Network.- Classification and Characteristics of TCM Syndromes of Chronic Respiratory Failure Based on Self-adaptive Fuzzy Inference System.- Implementation of the Pattern Matching System to Detect Flip Chip PCB Defects.- JPEG Steganalysis Using Estimated Image and Markov Model.- Applications of Two-Dimensional Heteroscedastic Discriminant Analysis in Face Recognition.- Intelligent Computing in Image Processing.- Multimodal Image Fusion Algorithm Using Dual-Tree Complex Wavelet Transform and Particle Swarm Optimization.- An Adaptive Method for Lane Marking Detection Based on HSI Color Model.- An Image Data Hiding Method Using Pixel-Based JND Model.- Intelligent Computing Applications Based on Eye Gaze: Their Role in Medical Image Interpretation.- Intelligent Computing in Communication and Computer Networks.- A Sequential Test Based Cooperative Spectrum Sensing Scheme Using Fuzzy Logic for Cognitive Radio Networks.- Solving Base Station Subsystem Assignment Problem in Mobile Communication Networks Using Hybridized Heuristic Algorithm.- A Sequential Cooperative Spectrum Sensing Scheme Based on Dempster Shafer Theory of Evidence.- High Resolution Direction Finding of Multiple Coherent Signals.- Extract and Maintain the Most Helpful Wavelet Coefficients for Continuous K-Nearest Neighbor Queries in Stream Processing.- A Neural Network-Based Cooperative Spectrum Sensing Scheme for Cognitive Radio Systems.- Intelligent Computing in Robotics.- Comparison of the Observability Indices for Robot Calibration considering Joint Stiffness Parameters.- Intelligent Computing in Computer Vision.- Human Computer Interaction Using Hand Gestures.- Smart Wheelchair Navigation Based on User's Gaze on Destination.- Entrance Detection of Building Component Based on Multiple Cues.- Intelligent Prediction and Time Series Analysis.- Developing an Evolutionary Neural Network Model for Stock Index Forecasting.- A Tower-Shadow Model for Wind Turbines Using a Wavelet-Prony Method.- Special Session on New Hand-Based Biometric Methods.- Contrast Enhancement and Metrics for Biometric Vein Pattern Recognition.- Feature Extraction Method for Contactless Palmprint Biometrics.- Special Session on Theories and Applications in Advanced Intelligent Computing.- Numerical Analysis for Stochastic Investment System with Poisson Jumps.- Quantum Collapsing Median Filter.- An Impulse C Application in the LDPC Decoding Algorithm.- Research on Algorithm of Parallel Garbage Collection Based on LISP 2 for Multi-core System.- Study on Anode Effect Prediction of Aluminium Reduction Applying Wavelet Packet Transform.- Rotating Machinery Fault Diagnosis Based on EMD-Approximate Entropy and LS-SVM.- Study of Applications Based on Measurement Technology in the Future Smart Grid.- Palm Line Extraction Using FRIT.- Fast ICA for Multi-speaker Recognition System.- Variable Universe Adaptive Fuzzy-PI Compound Control Applied in Maximum Power Point Tracking for Photovoltaic Energy Generation System.- Integrated and Automated Dielectric Measurement System at Millimeter Wavelengths.- Special Session on Search Based Software Engineering.- Software Security Testing of Web Applications Based on SSD.- Special Session on Bio-inspired Computing and Applications.- A Review of Bacterial Foraging Optimization Part I: Background and Development.- A Review of Bacterial Foraging Optimization Part II : Applications and Challenges.- Liquidity Risk Portfolio Optimization Using Swarm Intelligence.- Special Session on Advance in Dimensionality Reduction Methods and Its Applications.- Dimension Reduction with Semi-supervised Pairwise Covariance-Preserving Projection.- Special Session on Recent Advances in Medical Informatics.- Analysis of Impact Factors in Acupuncture for Patients with Migraine--Doubts on Prof. Andrew J Vickers' Conclusion.
• (source: Nielsen Book Data)

• Neural Networks.- Design of a Novel Six-Dimensional Force/Torque Sensor and Its Calibration Based on NN.- Incremental-Based Extreme Learning Machine Algorithms for Time-Variant Neural Networks.- Global Exponential Robust Stability of Hopfield Neural Networks with Reaction-Diffusion Terms.- Direct Inverse Model Control Based on a New Improved CMAC Neural Network.- Further Research on Extended Alternating Projection Neural Network.- Global Synchronization in an Array of Hybrid Coupling Neural Networks with Multiple Time-Delay Components.- Colour Image Segmentation Based on a Spiking Neural Network Model Inspired by the Visual System.- The White Noise Impact on the Optimal Performance of the Hopfield Neural Network.- The Study and Realization of Virtual Organization File System Based on DHT Technology.- Evolutionary Learning and Genetic Algorithms.- A Novel Quantum Genetic Algorithm for PID Controller.- Research on Hybrid Evolutionary Algorithms with Differential Evolution and GUO Tao Algorithm Based on Orthogonal Design.- An Improved Evolution Strategy for Constrained Circle Packing Problem.- Lecture Notes in Computer Science: Research on Multi-robot Avoidance Collision Planning Based on XCS.- Fuzzy Theory and Models.- An Integrated Method for the Construction of Compact Fuzzy Neural Models.- Scalarization of Type-1 Fuzzy Markov Chains.- Fuzzy Systems and Soft Computing.- Applying Fuzzy Differential Equations to the Performance Analysis of Service Composition.- Lattice Structures of Fuzzy Soft Sets.- A Predicate Formal System of Universal Logic with Projection Operator.- A Neuro-Evolutive Interval Type-2 TSK Fuzzy System for Volatile Weather Forecasting.- A Soft Computing Approach for Obtaining Transition Regions in Satellite Images.- Particle Swarm Optimization and Niche Technology.- Particle Swarm Optimization for Two-Stage Fuzzy Generalized Assignment Problem.- A Novel Cyclic Discrete Optimization Framework for Particle Swarm Optimization.- Economic Dispatch Considering Ancillary Service Based on Revised Particle Swarm Optimization Algorithm.- Particle Swarm Optimization-Based Extremum Seeking Control.- Image Contour Extraction Based on Ant Colony Algorithm and B-snake.- Supervised and Semi-supervised Learning.- An Improved Hidden Markov Model for Literature Metadata Extraction.- Discriminative Training of Subspace Gaussian Mixture Model for Pattern Classification.- Unsupervised and Reinforcement Learning.- A Stage by Stage Pruning Algorithm for Detecting the Number of Clusters in a Dataset.- Adaptive Independent Component Analysis by Modified Kernel Density Estimation.- Combinatorial and Numerical Optimization.- Cross System Bank Branch Evaluation Using Clustering and Data Envelopment Analysis.- He's Variational Iteration Method for Solving Convection Diffusion Equations.- GRASP for Low Autocorrelated Binary Sequences.- miRNA Target Prediction Method Based on the Combination of Multiple Algorithms.- Imperialistic Competitive Algorithm for Solving a Dynamic Cell Formation Problem with Production Planning.- Systems Biology and Computational Biology.- Genome-Wide DNA Methylation Profiling in 40 Breast Cancer Cell Lines.- GRIDUISS - A Grid Based Universal Immune System Simulator Framework.- Performance Comparison of Tumor Classification Based on Linear and Non-linear Dimensionality Reduction Methods.- Neural Computing and Optimization.- PH Optimal Control in the Clarifying Process of Sugar Cane Juice Based on DHP.- Nature Inspired Computing and Optimization.- Parameter-Free Deterministic Global Search with Simplified Central Force Optimization.- Comparison of Optimality and Robustness between SA, TS and GRASP Metaheuristics in FJSP Problem.- Hardware Emulation of Bacterial Quorum Sensing.- Knowledge Discovery and Data Mining.- Finding Research Community in Collaboration Network with Expertise Profiling.- The Ideal Data Representation for Feature Extraction of Traditional Malay Musical Instrument Sounds Classification.- Mining Reputation of Person/Product from Comment and Reply on UCC/Internet Article.- Interaction Analysis for Adaptive User Interfaces.- Unsupervised Subjectivity-Lexicon Generation Based on Vector Space Model for Multi-Dimensional Opinion Analysis in Blogosphere.- Enhancing Negation-Aware Sentiment Classification on Product Reviews via Multi-Unigram Feature Generation.- Building Associated Semantic Overlay for Discovering Associated Services.- Artificial Life and Artificial Immune Systems.- Immunity-Based Model for Malicious Code Detection.- Sparse Representation-Based Face Recognition for One Training Image per Person.- Semi-supervised Local Discriminant Embedding.- Orthogonal Discriminant Local Tangent Space Alignment.- Intelligent Computing in Image Processing.- Separating Pigment Components of Leaf Color Image Using FastICA.- Fast Algorithm for Multisource Image Registration Based on Geometric Feature of Corners.- Newborn Footprint Recognition Using Subspace Learning Methods.- Plant Classification Using Leaf Image Based on 2D Linear Discriminant Analysis.- Palmprint Recognition Combining LBP and Cellular Automata.- Dual Unsupervised Discriminant Projection for Face Recognition.- Applying Wikipedia-Based Explicit Semantic Analysis for Query-Biased Document Summarization.- Special Session on New Hand-Based Biometric Methods.- A New Approach for Vein Pattern-Based Recognition.- Study of Hand-Dorsa Vein Recognition.- DHV Image Registration Using Boundary Optimization.- Special Session on Recent Advances in Image Segmentation.- A Novel Level Set Model Based on Local Information.- A Multi-Descriptor, Multi-Nearest Neighbor Approach for Image Classification.- Orthogonal Locally Discriminant Projection for Palmprint Recognition.- Special Session on Theories and Applications in Advanced Intelligent Computing.- OPC UA Based Information Modeling for Distributed Industrial Systems.- Voting-Averaged Combination Method for Regressor Ensemble.- Face Recognition Using the Feature Fusion Technique Based on LNMF and NNSC Algorithms.- A PDOC Method for Topology Optimization Design.- Special Session on Search Based Software Engineering.- A Decision Support System Based on GIS for Grain Logistics Vehicle Routing Problem.- On Database Normalization Using User Interface Normal Form.- Special Session on Bio-inspired Computing and Applications.- Improved Particle Swarm Optimizers with Application on Constrained Portfolio Selection.- An Improved Image Rectification Algorithm Based on Particle Swarm Optimization.- Particle Swarm Optimizer Based on Small-World Topology and Comprehensive Learning.- Multi-Objective PSO Based on Evolutionary Programming.- Special Session on Advance in Dimensionality Reduction Methods and Its Applications.- Two-Dimensional Sparse Principal Component Analysis for Palmprint Recognition.- Discovery of Protein's Multifunction and Diversity of Information Transmission.- Special Session on Protein and Gene Bioinformatics: Methods and Applications.- Identification and Analysis of Binding Site Residues in Protein Complexes: Energy Based Approach.- Density Based Merging Search of Functional Modules in Protein-Protein Interaction (PPI) Networks.- Topology Prediction of ?-Helical and ?-Barrel Transmembrane Proteins Using RBF Networks.- Palmprint Recognition Based on Neighborhood Rough Set.- Increasing Reliability of Protein Interactome by Combining Heterogeneous Data Sources with Weighted Network Topological Metrics.- Predicting Protein Stability Change upon Double Mutation from Partial Sequence Information Using Data Mining Approach.- Inference of Gene Expression Regulation via microRNA Transfection.- A Residual Level Potential of Mean Force Based Approach to Predict Protein-Protein Interaction Affinity.
• (source: Nielsen Book Data)

The International Conference on Intelligent Computing (ICIC) was formed to provide an annual forum dedicated to the emerging and challenging topics in artificial intel- gence, machine learning, pattern recognition, image processing, bioinformatics, and computational biology. It aims to bring together researchers and practitioners from both academia and industry to share ideas, problems, and solutions related to the m- tifaceted aspects of intelligent computing. ICIC 2010, held in Changsha, China, August 18-21, 2010, constituted the 6th - ternational Conference on Intelligent Computing. It built upon the success of ICIC 2009, ICIC 2008, ICIC 2007, ICIC 2006, and ICIC 2005 that were held in Ulsan, Korea, Shanghai, Qingdao, Kunming and Hefei, China, respectively. This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was "Advanced Intelligent Computing Technology and Applications". Papers focusing on this theme were solicited, addressing theories, methodologies, and applications in science and technology.
(source: Nielsen Book Data)

• Evolutionary Computing and Genetic Algorithms
• Adaptive Routing Algorithm in Wireless Communication Networks Using Evolutionary Algorithm
• A New GA
• Based and Graph Theory Supported Distribution System Planning
• Sequencing Mixed-Model Assembly Lines with Limited Intermediate Buffers by a GA/SA-Based Algorithm
• Solving Vehicle Routing Problem Using Ant Colony and Genetic Algorithm
• Knowledge Discovery and Data Mining
• A Research on the Association of Pavement Surface Damages Using Data Mining
• An Integrated Method for GML Application Schema Match
• Application of Classification Methods for Forecasting Mid-Term Power Load Patterns
• Design of Fuzzy Entropy for Non Convex Membership Function
• Higher-Accuracy for Identifying Frequent Items over Real-Time Packet Streams
• Privacy Preserving Sequential Pattern Mining in Data Stream
• Methods of Computing Optimization
• A General k-Level Uncapacitated Facility Location Problem
• Fourier Series Chaotic Neural Networks
• Numerical Simulation and Experimental Study of Liquid-Solid Two-Phase Flow in Nozzle of DIA Jet
• Shape Matching Based on Ant Colony Optimization
• Fuzzy Systems and Soft Computing
• A Simulation Study on Fuzzy Markov Chains
• A Tentative Approach to Minimal Reducts by Combining Several Algorithms
• Ameliorating GM (1, 1) Model Based on the Structure of the Area under Trapezium
• Comparative Study with Fuzzy Entropy and Similarity Measure: One-to-One Correspondence
• Low Circle Fatigue Life Model Based on ANFIS
• New Structures of Intuitionistic Fuzzy Groups
• Intelligent Computing in Pattern Recognition
• An Illumination Independent Face Verification Based on Gabor Wavelet and Supported Vector Machine
• Hardware Deblocking Filter and Impact
• Medical Image Segmentation Using Anisotropic Filter, User Interaction and Fuzzy C-Mean (FCM)
• Medical Image Segmentation Using Fuzzy C-Mean (FCM), Learning Vector Quantization (LVQ) and User Interaction
• New Data Pre-processing on Assessing of Obstructive Sleep Apnea Syndrome: Line Based Normalization Method (LBNM)
• Recognition of Plant Leaves Using Support Vector Machine
• Region Segmentation of Outdoor Scene Using Multiple Features and Context Information
• Two-Dimensional Partial Least Squares and Its Application in Image Recognition
• Intelligent Computing in Bio/Cheminformatics
• A Novel Method of Creating Models for Finite Element Analysis Based on CT Scanning Images
• Accelerating Computation of DNA Sequence Alignment in Distributed Environment
• Predicting Protein Function by Genomic Data-Mining
• Tumor Classification Using Non-negative Matrix Factorization
• Intelligent Control and Automation
• A Visual Humanoid Teleoperation Control for Approaching Target Object
• An Intelligent Monitor System for Gearbox Test
• Development of Simulation Software for Coal-Fired Power Units Based on Matlab/Simulink
• Inconsistency Management
• Neural Network-Based Adaptive Optimal Controller
• A Continuous-Time Formulation
• On Improved Performance Index Function with Enhanced Generalization Ability and Simulation Research
• Intelligent Fault Diagnosis
• A Fault Diagnosis Approach for Rolling Bearings Based on EMD Method and Eigenvector Algorithm
• An Adaptive Fault-Tolerance Agent Running on Situation-Aware Environment
• Dynamic Neural Network-Based Pulsed Plasma Thruster (PPT) Fault Detection and Isolation for Formation Flying of Satellites
• Model-Based Neural Network and Wavelet Packets Decomposition on Damage Detecting of Composites
• Intelligent Computing in Communication
• A High Speed Mobile Courier Data Access System That Processes Database Queries in Real-Time
• A Scalable QoS-Aware VoD Resource Sharing Scheme for Next Generation Networks
• Brain Mechanisms for Making, Breaking, and Changing Rules
• Implementation of a Landscape Lighting System to Display Images
• Intelligent Sensor Networks
• Probability-Based Coverage Algorithm for 3D Wireless Sensor Networks
• Simulating an Adaptive Fault Tolerance for Situation-Aware Ubiquitous Computing
• A Hybrid CARV Architecture for Pervasive Computing Environments
• Intelligent Image/Document Retrievals
• Image and Its Semantic Role in Search Problem
• Color Image Watermarking Scheme Based on Efficient Preprocessing and Support Vector Machines
• Multiple Ranker Method in Document Retrieval
• Special Session on Image Processing, Analysis, and Vision Technology Based Intelligent Robot Systems
• An Elimination Method of Light Spot Based on Iris Image Fusion
• An Improved Model of Producing Saliency Map for Visual Attention System
• Multiple Classification of Plant Leaves Based on Gabor Transform and LBP Operator
• Research on License Plate Detection Based on Wavelet
• Stereo Correspondence Using Moment Invariants
• The Application of the Snake Model in Carcinoma Cell Image Segment
• Special Session on Data Mining and Fusion in Bioinformatics
• Data Clustering and Evolving Fuzzy Decision Tree for Data Base Classification Problems
• Multivariate Polynomials Estimation Based on GradientBoost in Multimodal Biometrics
• Special Session on Advances in Multidimensional Signal Processing
• An Introduction to Volterra Series and Its Application on Mechanical Systems
• Skin Detection from Different Color Spaces for Model-Based Face Detection
• Other Topics
• Applying Frequent Episode Algorithm to Masquerade Detection
• An Agent-Based Intelligent CAD Platform for Collaborative Design
• Design of a Reliable QoS Requirement Based on RCSM by Using MASQ Architecture
• Minimization of the Disagreements in Clustering Aggregation
• Prediction of Network Traffic Using Multiscale-Bilinear Recurrent Neural Network with Adaptive Learning
• Replay Attacks on Han et al.'s Chaotic Map Based Key Agreement Protocol Using Nonce
• The Short-Time Multifractal Formalism: Definition and Implement
• Modified Filled Function Method for Resolving Nonlinear Integer Programming Problem.

This book - in conjunction with the two volumes "LNCS 5226" and "LNAI 5227" - constitutes the refereed proceedings of the Fourth International Conference on Intelligent Computing, ICIC 2008, held in Shanghai, China in September 2008. The intelligent computing technology includes a range of techniques such as artificial intelligence, perceptual and pattern recognition, evolutionary and adaptive computing, informatics theories and applications, computational neuroscience and bioscience, soft computing, case based and constrained reasoning, agents, networking and computer supported co-operative working, human computer interface issues. ICIC theme unifies the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications.
(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)

• Title page; Preface; Contents; Introduction: Aspects of Artificial General Intelligence; A Collection of Definitions of Intelligence; Four Contemporary AGI Designs: A Comparative Treatment; A Foundational Architecture for Artificial General Intelligence; A Working Hypothesis for General Intelligence; From NARS to a Thinking Machine; Adaptive Algorithmic Hybrids for Human-Level Artificial Intelligence; Cognitive Map Dimensions of the Human Value System Extracted from Natural Language; Program Evolution for General Intelligence.

Examines the creation of software programs displaying broad, deep, human-style general intelligence. This work features papers presented at the 2006 AGIRI (Artificial General Intelligence Research Institute) workshop, which illustrates that it is a fit and proper subject for serious science and engineering exploration.

• 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)

• Data Mining and Machine Learning.- Planning and Combinatorial Optimization.- AI Applications.- Natural Language Processing.- Uncertainty and Preference Reasoning.- Agent Systems.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 30th Canadian Conference on Artificial Intelligence, Canadian AI 2017, held in Edmonton, AB, Canada, in May 2017. The 19 regular papers and 24 short papers presented together with 6 Graduate Student Symposium papers were carefully reviewed and selected from 62 submissions. The focus of the conference was on the following subjects: Data Mining and Machine Learning; Planning and Combinatorial Optimization; AI Applications; Natural Language Processing; Uncertainty and Preference Reasoning; and Agent Systems.
(source: Nielsen Book Data)

• Compressing Bayesian Networks: Swarm-Based Descent, Efficiency, and Posterior Accuracy.- De-Causalizing NAT-Modeled Bayesian Networks for Inference Efficiency.- A Novel Evaluation Methodology for Assessing Off-Policy Learning Methods in Contextual Bandits.- Synthesizing Controllers: On the Correspondence Between LTL Synthesis and Non-Deterministic Planning.- Logic-Based Benders Decomposition for Two-Stage Flexible Flow Shop Scheduling with Unrelated Parallel Machines.- Advice-Based Exploration in Model-Based Reinforcement Learning.- Deep Super Learner: A Deep Ensemble for Classification Problem.- One Single Deep Bidirectional LSTM Network for Word Sense Disambiguation of Text Data.- MedFact: Towards Improving Veracity of Medical Information in Social Media Using Applied Machine Learning.- Re-ranking Candidate Lists for Improved Lexical Induction.- Analysis of Social Media Posts for Early Detection of Mental Health Conditions.- Motor Bearing Fault diagnosis Using Deep Convolutional Neural Networks with 2D Analysis of Vibration Signal.- Mobile App for Detection of Counterfeit Banknotes.- A Multi-agent Framework for Understanding Addiction.- Infusing Domain Knowledge to Improve the Detection of Alzheimer's Disease from Everyday Motion Behavior.- An Incremental Machine Learning Algorithm for Nuclear Forensics.- MML-Based Approach for Determining the Number of Topics in EDCM Mixture Models.- Constrained Bayesian Optimization for Problems with Piece-wise Smooth Constraints.- Dimensionality Reduction and Visualization by Doubly Kernelized Unit Ball Embedding.- Accelerated Gradient and Block-wise Gradient Methods for Big Data Factorization.- Learning Belief Revision Operators.- Solving Constraint Satisfaction Problems Using Firey Algorithms.- An AI Planning-Based Approach to the Multi-Agent Plan Recognition Problem.- Predicting Transportation Modes of GPS Trajectories Using Feature Engineering and Noise Removal.- Prediction of Container Damage Insurance Claims for Optimized Maritime Port Operations.- Drug-Target Interaction Network Predictions for Drug Repurposing Using LASSO-based Regularized Linear Classification Model.- Optimal Scheduling for Smart Charging of Electric Vehicles Using Dynamic Programming.- Combining MCTS and A3C for Prediction of Spatially Spreading Processes in Forest Wildfire Settings.- Text-based Detection of Unauthorized Users of Social Media Accounts.- N-gram Based Approach for Automatic Prediction of Essay Rubric Marks.- Matching Resumes to Job Descriptions with Stacked Models.- Towards a Comprehensive Evaluation of Recommenders: A Cognition-based Approach.- A Sentence-level Sparse Gamma Topic Model for Sentiment Analysis.- Topic Detection and document Similarity on Financial News.- Software Defect Prediction from Code Quality Measurements via Machine Learning.- Automated Scheduling: Reinforcement Learning Approach to Algorithm Policy Learning.- Estimating Vineyard Grape Yield from Images.- Real-time Deep Learning Pedestrians Classification on a Micro-controller.- A Unified Evaluation Framework for Recommender Systems.- Early Detection of Alzheimer's Disease Using Deep Learning.- Learning with Prior Domain Knowledge and Insufficient Annotated Data.- Predicting Crime Using Spatial Features.- A Tool for Defining and Simulating Storage Strategies on the Smart Grid.- Decision Assist for Self-Driving Cars.- Rule Mining and Prediction Using the Flek Machine
• A New Machine Learning Engine.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 31th Canadian Conference on Artificial Intelligence, Canadian AI 2018, held in Toronto, ON, Canada, in May 2018. The 16 regular papers and 18 short papers presented together with 7 Graduate Student Symposium papers and 4 Industry Track papers were carefully reviewed and selected from 72 submissions. The focus of the conference was on artificial intelligence research and advanced information and communications technology.
(source: Nielsen Book Data)

• An intelligent image-based computer-aided education system: the prototype BIRDS / A.A. David, O. Thiery & M. Crehange
• PLAYMAKER: a knowledge-based approach to characterizing hydrocarbon plays / G. Biswas [and others]
• An expert system for interpreting mesoscale features in oceanographic satellite images / N. Krishnakumar [and others]
• An expert system for tuning particle beam accelerators / D.L. Lager, H.R. Brand & W.J. Maurer
• Expert system approach to assessments of bleeding predispositions in tonsillectomy/adenoidectomy patients / N.J. Pizzi & J.M. Gerrard
• Expert system approach using graph representation and analysis for variable-stroke internal-combustion engine design / S.N.T. Shen, M.S. Chew & G.F. Issa
• A comparison of two new techniques for conceptual clustering / S.L. Crawford & S.K. Souders
• Querying an object-oriented database using free language / P. Trigano [and others]
• Adaptive planning for air combat maneuvering / I.C. Hayslip, J.P. Rosenking & J. Filbert
• AM/AG model: a hierarchical social system metaphor for distributed problem solving / D.G. Shin & J. Leone
• CAUSA
• A tool for model-based knowledge acquisition / W. Dilger & J. Moller
• PRIOPS: a real-time production system architecture for programming and learning in embedded systems / D.E. Parson & G.D. Blank.

This volume contains a well-balanced set of applications and theory papers in artificial intelligence advances. The applications papers each discuss a system that is (or is close to being) a fielded system that solves real problems using one or more AI techniques. They cover areas such as education, physics, energy, control, medicine and mechanical engineering.The theory papers, representing recent advances in various theoretical aspects of AI technology, concern themselves with "building block" issues, i.e. theories, algorithms, architectures, and software tools that can or will be used for modules within future systems. The topics covered are: clustering, natural language, adaptive algorithms, distributed processing, knowledge acquisition, and systems programming.
(source: Nielsen Book Data)

• Intro; Preface; About the Subject; About the Book; Objectives of the Book; Organization of the Book; Book Features; Audience; Acknowledgements; Contents; Part I Advances in Chaos Theory; A Novel Design Approach of a Nonlinear Resistor Based on a Memristor Emulator; 1 Introduction; 2 Memristor
• A Brief Review; 2.1 The Invention; 2.2 The First Physical Model of the Memristor; 3 Memristor's Emulators; 3.1 Related Works; 3.2 The Proposed Memristor Emulator; 4 Implementation of a Nonlinear Resistor Based on the Memristor Emulator; 5 Nonlinear Circuit Based on the Proposed Nonlinear Resistor

• 5.1 Numerical Simulation Results5.2 Simulation Results with Multisim; 6 Conclusion; References; Dynamics, Synchronization and SPICE Implementation of a Memristive System with Hidden Hyperchaotic Attractor; 1 Introduction; 2 Related Work; 3 Model of the Memristive System; 4 Dynamics and Properties of the Memristive System; 5 Adaptive Anti-synchronization of the Memristive System; 6 Circuit Realization of the Memristive System; 7 Conclusion; References; Synchronization of Fractional Chaotic and Hyperchaotic Systems Using an Extended Active Control; 1 Introduction; 2 Related Work

• 3 Preliminaries4 Synchronization Techniques; 4.1 Equations Not Involving Delay; 4.2 Equations Involving Delay; 5 Examples; 5.1 Synchronization of Chaotic Fractional Liu System; 5.2 Synchronization of Hyperchaotic Fractional New System; 5.3 Synchronization of Fractional Order Delay System; 6 Discussion; 7 Conclusion; References; A Novel 4-D Hyperchaotic Thermal Convection System and Its Adaptive Control; 1 Introduction; 2 A Novel 4-D Hyperchaotic Thermal Convection System; 3 Analysis of the Novel 4-D Hyperchaotic Thermal Convection System; 3.1 Dissipativity; 3.2 Equilibrium Points

• 3.3 Rotation Symmetry About the x3-axis3.4 Invariance; 3.5 Lyapunov Exponents and Kaplan-Yorke Dimension; 4 Adaptive Control of the Novel Hyperchaotic Thermal Convection System; 5 Adaptive Synchronization of the Identical Novel Hyperchaotic Systems; 6 Conclusions; References; Synchronization of Chaotic Dynamical Systems in Discrete-Time; 1 Introduction; 2 Complete Synchronization for Arbitrary 3D Quadratic Maps; 2.1 General Synchronization Approach; 2.2 Illustrative Example; 2.3 Conclusion; 3 Complete Synchronization Criterions for N-Dimensional Maps

• 3.1 Synchronization Criterion Via Controlling(bij) 1leqi, jleqn3.2 Synchronization Criterion Via Controlling (bii)1leqileqn; 3.3 Numerical Application; 3.4 Conclusion; 4 Matrix Projective Synchronization for Different Dimensional Maps; 4.1 Matrix Projective Synchronization Criterion; 4.2 Simulation Example; 4.3 Conclusion; 5 Inverse Matrix Projective Synchronization Between n-D and m-D Maps; 5.1 Synchronization Results; 5.2 Numerical Results; 5.3 Conclusion; 6 Synchronization in Different Dimensions Using Two Scaling Matrices

The book reports on the latest advances in and applications of chaos theory and intelligent control. Written by eminent scientists and active researchers and using a clear, matter-of-fact style, it covers advanced theories, methods, and applications in a variety of research areas, and explains key concepts in modeling, analysis, and control of chaotic and hyperchaotic systems. Topics include fractional chaotic systems, chaos control, chaos synchronization, memristors, jerk circuits, chaotic systems with hidden attractors, mechanical and biological chaos, and circuit realization of chaotic systems. The book further covers fuzzy logic controllers, evolutionary algorithms, swarm intelligence, and petri nets among other topics. Not only does it provide the readers with chaos fundamentals and intelligent control-based algorithms; it also discusses key applications of chaos as well as multidisciplinary solutions developed via intelligent control. The book is a timely and comprehensive reference guide for graduate students, researchers, and practitioners in the areas of chaos theory and intelligent control.

• Enough Questions for Everybody
• Understanding Collective Intelligence
• Predicting Asset Value through Twitter Buzz
• Adding Value with Collective Intelligence
• A Reference Framework for Business Models for User-Generated Content
• Collective Intelligence Model: How to Describe Collective Intelligence
• The Participatory Roles Play Simulation in a Social and Collective Learning Context
• A Complex Network Analysis of the Weighted Graph of the Web2.0 Service Network
• How Collective Intelligence Redefines Education
• On Presence, Collective Performance and Assumptions of Causality
• Preservation of Enterprise Engineering Processes by Social Collaboration Software
• Univector Field Method Based Multi-robot Navigation for Pursuit Problem
• Harvesting Domain-Specific Data Resources for Enhanced After-Sales Intelligence in Car Industry.

Collective intelligence has become an attractive subject of interest for both academia and industry. More and more conferences and workshops discuss the impact of the users' motivation to participate in the value creation process, the enabling role of leading-edge information and communication technologies and the need for better algorithms to deal with the growing amount of shared data. There are many interesting and challenging topics that need to be researched and discussed with respect to knowledge creation, creativity and innovation processes carried forward in the emerging communities of practice. COLLIN is on the path to become the flagship conference in the areas of collective intelligence and ICT-enabled social networking. We were delighted to again receive contributions from different parts of the world including Australia, Europe, Asia, and the United States. Encouraged by the positive response, we plan COLLIN 2012 to be held next year end of August at FernUniverstität in Hagen. In order to guarantee the quality of the event, each paper went through a doubleblind review process. The reviews concentrated on originality, quality and relevance of the paper topic to the symposium. In addition, we invited a few renowned experts in the field to contribute to the success of the symposium with outstanding papers reporting on their most recent research. Our special thanks go to the authors for submitting their papers, to the international program committee members, and to numerous reviewers who did an excellent job in guaranteeing that the papers in this volume are of very high quality.

• 1. A quest for granular computing and logic processing / W. Pedrycz
• 2. Abstraction and linguistic analysis of conventional numerical dynamic systems / F.-Y. Wang
• 3. Slicing: a distributed learning approach / S.A. Eschrich and L.O. Hall
• 4. Marginal learning algorithms in statistical machine learning / Q. Tao and J. Wang
• 5. Constraint handling in genetic algorithm for optimization / G.G. Yen
• 6. Hybrid PSO-EA algorithm for training feedforward and recurrent neural networks for challenging problems / X. Cai, G.K. Venayagamoorthy, and D.C. Wunsch II
• 7. Modular wavelet-fuzzy networks / Y. Lin and F.-Y. Wang
• 8. Ant colony algorithms: the state-of-the-art / J. Zhang, J. Xu, and S. Zhang
• 9. Motif discoveries in DNA and protein sequences using self-organizing neural networks / D. Liu and X. Xiong
• 10. Computational complexities of combinatorial problems with applications to reverse engineering of biological networks / P. Berman, B. DasGupta, and E. Sontag
• 11. Advances in fingerprint recognition algorithms with application / J. Tian, X. Chen, Y. Zhang, and X. Yang
• 12. Adaptation and predictive control observed in neuromuscular control systems / J. He. 13. Robust adaptive approximation based backstepping via localized adaptive bonding / Y. Zhao and J.A. Farrell
• 14. Dynamically connected fuzzy single input rule modules and application to underactuated systems / J. Yi, N. Yubazaki, and K. Hirota.

Computational Intelligence (CI) is a recently emerging area in fundamental and applied research, exploiting a number of advanced information processing technologies that mainly embody neural networks, fuzzy logic and evolutionary computation. With a major concern to exploiting the tolerance for imperfection, uncertainty, and partial truth to achieve tractability, robustness and low solution cost, it becomes evident that composing methods of CI should be working concurrently rather than separately. It is this conviction that research on the synergism of CI paradigms has experienced significant growth in the last decade with some areas nearing maturity while many others remaining unresolved. This book systematically summarizes the latest findings and sheds light on the respective fields that might lead to future breakthroughs.

• Introduction to support vector learning
• roadmap. Part 1 Theory: three remarks on the support vector method of function estimation, Vladimir Vapnik
• generalization performance of support vector machines and other pattern classifiers, Peter Bartlett and John Shawe-Taylor
• Bayesian voting schemes and large margin classifiers, Nello Cristianini and John Shawe-Taylor
• support vector machines, reproducing kernel Hilbert spaces, and randomized GACV, Grace Wahba
• geometry and invariance in kernel based methods, Christopher J.C. Burges
• on the annealed VC entropy for margin classifiers - a statistical mechanics study, Manfred Opper
• entropy numbers, operators and support vector kernels, Robert C. Williamson et al. Part 2 Implementations: solving the quadratic programming problem arising in support vector classification, Linda Kaufman
• making large-scale support vector machine learning practical, Thorsten Joachims
• fast training of support vector machines using sequential minimal optimization, John C. Platt. Part 3 Applications: support vector machines for dynamic reconstruction of a chaotic system, Davide Mattera and Simon Haykin
• using support vector machines for time series prediction, Klaus-Robert Muller et al
• pairwise classification and support vector machines, Ulrich Kressel. Part 4 Extensions of the algorithm: reducing the run-time complexity in support vector machines, Edgar E. Osuna and Federico Girosi
• support vector regression with ANOVA decomposition kernels, Mark O. Stitson et al
• support vector density estimation, Jason Weston et al
• combining support vector and mathematical programming methods for classification, Bernhard Scholkopf et al.
• (source: Nielsen Book Data)

The Support Vector Machine is a powerful new learning algorithm for solving a variety of learning and function estimation problems, such as pattern recognition, regression estimation, and operator inversion. The impetus for this collection was a workshop on Support Vector Machines held at the 1997 NIPS conference. The contributors, both university researchers and engineers developing applications for the corporate world, form a Who's Who of this exciting new area.ContributorsPeter Bartlett, Kristin P. Bennett, Christopher J.C. Burges, Nello Cristianini, Alex Gammerman, Federico Girosi, Simon Haykin, Thorsten Joachims, Linda Kaufman, Jens Kohlmorgen, Ulrich Kressel, Davide Mattera, Klaus-Robert Muller, Manfred Opper, Edgar E. Osuna, John C. Platt, Gunnar Ratsch, Bernhard Schoelkopf, John Shawe-Taylor, Alexander J. Smola, Mark O. Stitson, Vladimir Vapnik, Volodya Vovk, Grace Wahba, Chris Watkins, Jason Weston, Robert C. Williamson.
(source: Nielsen Book Data)
The Support Vector Machine is a powerful new learning algorithm for solving a variety of learning and function estimation problems, such as pattern recognition, regression estimation, and operator inversion. The impetus for this collection was a workshop on Support Vector Machines held at the 1997 NIPS conference. The contributors, both university researchers and engineers developing applications for the corporate world, form a Who's Who of this exciting new area.
(source: Nielsen Book Data)

• ADVANCES IN KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS; Frontiers in Artificial Intelligence and Applications; Preface; Contents; Set of Experience and Experiential Decisional DNA; An Estimation of Distribution Algorithm for Solving the Quay Crane Scheduling Problem with Availability Constraints; Performance comparison of non-RNN and RNN in Emergence of Discrete Decision Making through Reinforcement Learning; Genetic Algorithm Solving Orienteering Problem in Large Networks; Optimisation of Ensemble Classifiers using Genetic Algorithm.

• A Learning Based Evolutionary Algorithm For Distributed Multi-Depot VRPPositive Predictive Value based dynamic K-Nearest Neighbor; An Analysis of Clustering Approaches to Distributed Learning on Heterogeneously Distributed Datasets; Unsupervised Discretization Method based on Adjustable Intervals; Evaluation of Random Subspace and Random Forest Regression Models Based on Genetic Fuzzy Syste; A logic for strategies in persuasion dialogue games; Multi-Agent Logic based on Temporary Logic TS4Kn serving Web Search; LMT: A Lightweight Logical Framework for Multi-agent Systems.

• Adaptive organization for cooperative systemsAn optimal tactic for intelligent agents to conduct search & detection operations based on multiple look angles; A complex system approach for a reliable Smart Grid modeling; A Comparison Analysis of Consensus Determining Using One and Two-level Methods; Multi-Agent Logic with Distances, Uncertainty and Interaction Based on Linear Temporal Frames; A framework for handling fuzzy temporal events; On the Continuous Evaluation of the Macrostructure of Sleep.

• Entropic Dimensionality Reduction in Discriminating Between Alzheimer's Disease and Vascular DementiaMeaning Judgment Method for Alphabet Abbreviation Using the Association Mechanism; Method of Constructing the Integral OLAP-model based on Formal Concept Analysis; Semantically Enhanced Text Stemmer (SETS) for Document Clustering; Prosaico: Characterisation of objectives within the scope of an intelligent system for sport advising; Exploiting the Self-Organizing Financial Stability Map; Knowledge-Driven Method for Object Qualification in 3D Point Cloud Data.

• SAC3̂
• A Rule-Based System to Include Context in the Durability Analysis of Civil StructuresPredicting the Final Result of Sporting Events Based on Changes in Bookmaker Odds; A Novel Channel Estimation Scheme Combining Adaptive Supervised and Unsupervised Algorithms; Reference signal cancellation in passive radar using Volterra-Wiener class filter with dynamic structure; Prosodic feature normalization for emotionre cognition by using synthesized speech; Regularity Dimension of Medical Images; Automatic Scoring of Shooting Targets with Tournament Precision.

The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification-that is, a scale parameter-rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms. The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba.
(source: Nielsen Book Data)

The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification-that is, a scale parameter-rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms. The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba.
(source: Nielsen Book Data)

The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification-that is, a scale parameter-rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms. The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba.
(source: Nielsen Book Data)

• Cover; Title page; Preface; Organizing committee; List of chapter contributors; Contents; 1 An Overview of Statistical Learning Theory; 2 Best Choices for Regularization Parameters in Learning Theory: On the Bias-Variance Problem; 3 Cucker Smale Learning Theory in Besov Spaces; 4 High-dimensional Approximation by Neural Networks; 5 Functional Learning through Kernels; 6 Leave-one-out Error and Stability of Learning Algorithms with Applications; 7 Regularized Least-Squares Classification; 8 Support Vector Machines: Least Squares Approaches and Extensions.

This text details advances in learning theory that relate to problems studied in neural networks, machine learning, mathematics and statistics.

• Cover; Title page; Contents; Retriever Prototype of a Case Based Reasoning: A Study Case; Dynamic Compaction Process of Metal Powder Media within Dies; Automated Theorem Proving for Many-sorted Free Description Theory Based on Logic Translation; Annotated Logic and Negation as Failure; Multi-agent System for Distribution System Operation; ArTbitrariness: Putting Computer Creativity to Work in Aesthetic Domains; An Overview of Fuzzy Numbers and Fuzzy Arithmetic; The Brain and Arithmetic Calculation; Evolving Arithmetical Knowledge in a Distributed Intelligent Processing System.

Logic (both classical and non-classical) is being increasingly related with other fields in almost every scientific discipline and human activity. This work covers its role in artificial intelligence, robotics, informatics, technology, and correlated themes.