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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)

• Cover
• Contents
• Preface
• 1 Introduction
• 1.1 Background
• 2 Mobile Agent System
• 2.1 Components of a Mobile Agent System
• 2.2 Characteristics and Advantages of a Mobile Agent System
• 3 Attacks and Countermeasures of Software System Security
• 3.1 General Security Objectives
• 3.2 Types of Attacks
• 3.2.1 Attacks against availability
• 3.2.2 Attacks against confidentiality
• 3.2.3 Attacks against integrity
• 3.2.4 Attacks against miscellaneous security objectives
• 3.3 Countermeasures of Attacks
• 3.3.1 Authentication
• 3.3.2 Access control
• 3.3.3 Audit and intrusion detection
• 3.3.4 Cryptography
• 3.3.5 Firewall
• 3.3.6 Anti-virus software
• 4 Security Issues in a Mobile Agent System
• 4.1 Security Issues in a Mobile Agent System
• 4.1.1 Possible attacks to a mobile agent system
• 4.1.2 Security requirements for a secure mobile agent system &

A mobile agent system could be attacked by malicious agents, platforms and third parties. Mobile agents simply offer greater opportunities for abuse and misuse, which broadens the scale of threats significantly. In addition, since mobile agents have some unique characteristics such as their mobility, security problems have become more complicated in these systems. These security problems have become a bottleneck in the development and maintenance of mobile agent systems, especially in security sensitive applications such as electronic commerce. This book introduces the concept and structure of mobile agent systems and discusses various attacks and countermeasures. The emphasis is on the formal modeling and analysis of secure mobile agent systems and their applications.

• Mobile Agent System
• Attack and Countermeasure
• Security Issues in a Mobile Agent System
• Formal Models
• A Formal Framework of a Generic Secure Mobile Agent System
• Translating the EEOS Model to Colored Petri Net Model
• Simulation and Analysis of the Extended Elementary Object System Model of a Secure Mobile Agent System
• A Case Study in Electronic Commerce
• A Case Study in E-Auction System.
• (source: Nielsen Book Data)

A mobile agent system could be attacked by malicious agents, platforms and third parties. Mobile agents simply offer greater opportunities for abuse and misuse, which broadens the scale of threats significantly. In addition, since mobile agents have some unique characteristics such as their mobility, security problems have become more complicated in these systems. These security problems have become a bottleneck in the development and maintenance of mobile agent systems, especially in security sensitive applications such as electronic commerce. This book introduces the concept and structure of mobile agent systems and discusses various attacks and countermeasures. The emphasis is on the formal modeling and analysis of secure mobile agent systems and their applications.
(source: Nielsen Book Data)

• Cover; Title page; Preface; Acknowledgments; Contents; I. Data Collection; Toward Active Mining from On-line Scientific Text Abstracts Using Pre-existing Sources; Data Mining on the WAVEs
• Word-of-mouth-Assisting Virtual Environments; Immune Network-based Clustering for WWW Information Gathering/Visualization; Interactive Web Page Retrieval with Relational Learning-based Filtering Rules; Monitoring Partial Update of Web Pages by Interactive Relational Learning; Context-based Classification of Technical Terms Using Support Vector Machines.

Focusing on data mining, this work is a joint effort from researchers in Japan, and includes a report on the forefront of data collection, user-centred mining and user interaction/reaction. It offers an overview of modern solutions with real-world applications, sharing hard-learned experiences.

• Introduction.- Logic and Inferencing.- First Order Predicate Logic.- Rule Based Reasoning.- Introduction to Prolog Language.- Knowledge Representation and Reasoning.- Networks Based Representation.- State Space Search.- Heuristic Search.- Constraint Satisfaction Problems.- Search and Game Playing.- Reasoning in Uncertain Environments.- Planning.- Natural Language Processing.- Machine Learning.- Machine Vision.- Bibliography.
• (source: Nielsen Book Data)

Fundamentals of Artificial Intelligence introduces the foundations of present day AI and provides coverage to recent developments in AI such as Constraint Satisfaction Problems, Adversarial Search and Game Theory, Statistical Learning Theory, Automated Planning, Intelligent Agents, Information Retrieval, Natural Language & Speech Processing, and Machine Vision. The book features a wealth of examples and illustrations, and practical approaches along with the theoretical concepts. It covers all major areas of AI in the domain of recent developments. The book is intended primarily for students who major in computer science at undergraduate and graduate level but will also be of interest as a foundation to researchers in the area of AI.
(source: Nielsen Book Data)

• A Combined Approach for Ontology Enrichment from Textual and Open Data.- C-SPARQL Extension for Sampling RDF Graphs Streams.- Efficiency Analysis of ASP Encodings for Sequential Pattern Mining Tasks.
• (source: Nielsen Book Data)

This book is a collection of representative and novel works in the field of data mining, knowledge discovery, clustering and classification. Discussing both theoretical and practical aspects of "Knowledge Discovery and Management" (KDM), it is intended for researchers interested in these fields, including PhD and MSc students, and researchers from public or private laboratories. The contributions included are extended and reworked versions of six of the best papers that were originally presented in French at the EGC'2016 conference held in Reims (France) in January 2016. This was the 16th edition of this successful conference, which takes place each year, and also featured workshops and other events with the aim of promoting exchanges between researchers and companies concerned with KDM and its applications in business, administration, industry and public organizations. For more details about the EGC society, please consult egc.asso.fr.
(source: Nielsen Book Data)

• Fuzzy Numbers.- Dual Numbers.- Fuzzy Dual Numbers.- Fuzzy Dual Linear Matrix Inequalities.- Fuzzy Estimation of Distribution Matrices.- Fuzzy Dual Entropy and Applications.- Fuzzy Dual Programming.
• (source: Nielsen Book Data)

This is the first book focusing exclusively on fuzzy dual numbers. In addition to offering a concise guide to their properties, operations and applications, it discusses some of their advantages with regard to classical fuzzy numbers, and describes the most important operations together with a set of interesting applications in e.g. optimization, decision-making and system design. The book provides students, researchers and professionals the necessary theoretical background to apply this particular subset of fuzzy numbers to decision-making problems involving uncertainty. Further, it shows how to solve selected engineering and management problems and includes detailed numerical examples.
(source: Nielsen Book Data)

• Introduction.- Pairwise Comparison Matrices.- Fuzzy Set Theory.- Fuzzy Pairwise Comparison Matrices.- Incomplete Large-dimensional Pairwise Comparison Matrices.- Discussion and Future Research. .
• (source: Nielsen Book Data)

This book offers the first comprehensive and critical literature review of fuzzy pairwise comparison methods derived from methods originally developed for crisp pairwise comparison matrices. It proposes new fuzzy extensions of these methods and provides a detailed study of the differences and analogies between all the reviewed methods, as well as a detailed description of their drawbacks, with the help of many numerical examples. In order to prevent the drawbacks related to the reviewed fuzzy pairwise comparison methods, the book introduces constrained fuzzy arithmetic in fuzzy extension of the pairwise comparison methods. It proposes new fuzzy pairwise comparison methods based on constrained fuzzy arithmetic and critically compares them with the reviewed methods. It describes the application of the newly developed methods to incomplete large-dimensional pairwise comparison matrices showcased in a real-life case study. Written for researchers, graduate and PhD students interested in multi-criteria decision making methods based on both crisp and fuzzy pairwise comparison matrices, this self-contained book offers an overview of cutting-edge research and all necessary information to understand the described tools and use them in real-world applications.
(source: Nielsen Book Data)

• Intro; Title Page; Contents; Introduction; Motivation; Problem Statement; Challenges and Overview of the State-of-the-Art; Challenges for Efficient SPARQL Query Processing; Challenges for Effective SPARQL Query Processing; Hypotheses and Research Questions; Contributions; Outline; Foundations of Linked Data Management; Linked Data; The Resource Description Framework (RDF); Querying RDF Data: The SPARQL Query Language; Querying RDF Data on the Web; URI Dereferencing and Link Traversal; SPARQL Endpoints; Linked Data Fragments; Foundations of Query Processing; Query Optimization

• Adaptive Query ProcessingAdaptive Query Processing over Linked Data; Introduction; Research Questions; Contributions; Structure of the Chapter; Motivating Example; Related Work; Adaptive Link Traversal Approaches; Adaptive Query Processing Against SPARQL Endpoints; Query Processing Approaches Against TPF Servers; The nLDE Approach; nLDE Query Optimizer; Estimation of Query Plan Cardinalities; Placing Physical Operators; Building Query Tree Plans; nLDE Optimizer: Algorithm Description; Complexity of the nLDE Query Optimizer; nLDE Adaptive Routing Query Engine; Adaptive Operators; Eddies

• Network of Linked Data Eddies (nLDE)Termination of nLDE; Correctness of nLDE; Routing Policies; Routing Policy from Eddies to Adaptive Operators; Routing Policy from Adaptive Operators to Eddies; Experimental Study; Experimental Settings; Efficiency of the nLDE Optimizer; Effectiveness of the nLDE Optimizer; Impact of the nLDE Routing-based Adaptivity on Execution Time in Perfect Networks; Effectiveness of the nLDE Routing-based Adaptivity Under the Presence of Network Delays; Summary and Future Work; Foundations of Crowdsourcing; Overview; Types of Crowdsourcing; Microtasks; Contests

• Crowdsourcing WorkflowsHybrid Crowdsourcing Workflows; Human-based Workflow: Find-Fix-Verify; Crowdsourcing Query Answer Completeness over Linked Data; Introduction; Research Questions; Contributions; Structure of the Chapter; Motivating Example; Related Work; Hybrid Query Processing for Relational Data; Crowd-based Linked Data Management Applications; Web Data Quality Assessment; The HARE Approach; Problem Definition; Proposed Solution; RDF Completeness Model; Representation of the Crowd Knowledge; Crowd Contradiction; Crowd Unknownness; HARE Microtask Manager; User Interface Generator

• Microtask ExecutorHARE Query Optimizer; Complexity of the HARE Query Optimizer; HARE Query Engine; A SPARQL Fuzzy Set Semantics; HARE BGP Executor; Complexity of HARE Query Evaluation; Experimental Study; Experimental Settings; HARE Crowdsourcing Capabilities; Size of Query Answer; Quality of Crowd Answers; Crowd Response Time; Summary and Future Work; Crowdsourcing Linked Data Quality Issues; Introduction; Research Questions; Contributions; Structure of the Chapter; Preliminaries: Linked Data Quality Issues; Related Work; Crowdsourcing in Linked Data Management; Web Data Quality Assessment

• Sequential Predictive Scheduling in Partitioned Data Domains.- Wildfire Optimizations in Modeling and Calibrations for Bulgarian Test Cases.- Process Control with the Variability Constraints.- Comparison of Different ACO Start Strategies Based on InterCriteria Analysis.- Evolutionary Approach for Tuning of Longwall Scraper Conveyor Model.- Solving Graph Partitioning Problems with Parallel Metaheuristics.- Comparison of Selected Fuzzy PSO Algorithms.
• (source: Nielsen Book Data)

This book presents new optimization approaches and methods and their application in real-world and industrial problems, and demonstrates how many of the problems arising in engineering, economics and other domains can be formulated as optimization problems. Constituting a comprehensive collection of extended contributions from the 9th International Workshop on Computational Optimization (WCO) held in Gdansk, Poland, September 11-14, 2016, the book discusses important applications such as job scheduling, wildfire modeling, parameter settings for controlling different processes, capital budgeting, data mining, finding the location of sensors in a given network, identifying the conformation of molecules, algorithm correctness, decision support system, and computer memory management. Further, it shows how to develop algorithms for these based on new intelligent methods like evolutionary computations, ant colony optimization and constraint programming. The book is a valuable resource for researchers and practitioners alike.
(source: Nielsen Book Data)

• A Review of Soft Computing Techniques in Maritime Logistics and its Related Fields.- Intelligent Data Analysis, Soft Computing and Imperfect Data.- Soft Computing Methods in Transport and Logistics.- Applications of Soft Computing in Intelligent Transportation Systems.- Fuzzy Cognitive Maps based Models for Pattern Classification: Advances and Challenges.- A proposal of on-line detection of new faults and automatic learning in fault diagnosis.- Fuzzy Portfolio Selection Models for dealing with Investor's Preferences.- Gradual numbers and fuzzy solutions to fuzzy optimization problems.- A modal account of preference in a fuzzy setting.- The single parameter family of Gini Bonferroni welfare functions and the binomial decomposition, transfer sensitivity and positional transfer sensitivity.
• (source: Nielsen Book Data)

This book offers a timely snapshot of current soft-computing research and solutions to decision-making and optimization problems, which are ubiquitous in the current social and technological context, addressing fields including logistics, transportation and data analysis. Written by leading international experts from the United States, Brazil and Cuba, as well as the United Kingdom, France, Finland and Spain, it discusses theoretical developments in and practical applications of soft computing in fields where these methods are crucial to obtaining better models, including: intelligent transportation systems, maritime logistics, portfolio selection, decision- making, fuzzy cognitive maps, and fault detection. The book is dedicated to Professor Jose L. Verdegay, a pioneer who has been actively pursuing research in fuzzy sets theory and soft computing since 1982, in honor of his 65th birthday.
(source: Nielsen Book Data)

• Sequence Classification based on Active Learning.- Using MongoDB Databases For Training and Combining Intrusion Detection Datasets.- Service Oriented Network Management with Knowledge-Based Network Management System in Fluctuating Environment.- A Topic Structuration Method on Time Series for a Meeting from Text Data.- Joint Embedding of Hierarchical Structure and Context for Entity Disambiguation.- Automatic Optimization of OpenCL-Based Stencil Codes for FPGAs.- Should Duration and Team Size be Used for Effort Estimation?.- A Lifelog Data Portfolio for Privacy Protection based on Dynamic Data Attributes in a Lifelog Service.- Estimation of Emotional Scene from Lifelog Videos in Consideration of Intensity of Various Facial Expressions.- Developing a Framework to Support Designing of Active Learning Class.- Where Do Drivers Look When Driving in a Foreign Country?.- On Password Strength: A Survey and Analysis.- Probabilistic Model-Based Multistep Crossover Considering Dependency between Nodes in Tree Optimization.- Digital Watermark Design for Two-dimensional Codes Displayed on Smart Phone Screen Using Multi-Objective Optimization and Optical Simulation.
• (source: Nielsen Book Data)

This book gathers 14 of the most promising papers presented at the 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2017), which was held on June 26-28, 2017 in Kanazawa, Japan. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the various fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. The book presents research findings concerning all aspects (theory, applications and tools) of computer and information science, and discusses the practical challenges encountered along the way, as well as the solutions adopted to solve them.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Intelligence computer data analysis techniques
• 3. The fundamentals of Cognitive Informatics
• 4. Cognitive information systems
• 5. The fundamentals of management sciences
• 6. Intelligent cognitive information systems in management application
• 7. Summary.
• (source: Nielsen Book Data)

Cognitive Information Systems in Management Sciences summarizes the body of work in this area, taking an analytical approach to interpreting the data, while also providing an approach that can be used for practical implementation in the fields of computing, economics, and engineering. Using numerous illustrative examples, and following both theoretical and practical results, Dr. Lidia Ogiela discusses the concepts and principles of cognitive information systems, the relationship between intelligent computer data analysis, and how to utilize computational intelligent approaches to enhance information retrieval. Real world implantation use cases round out the book, with valuable scenarios covering management science, computer science, and engineering. Indexing: The books of this series are submitted to EI-Compendex and SCOPUS.
(source: Nielsen Book Data)

Annotation The Symposium series refers to all apects of theory, algorithm design, applications and emerging techniques related to computational intelligence.

• Part I: Online learning of a weighted selective naive Bayes classifier with non-convex optimization
• On making skyline queries resistant to outliers
• Adaptive Down-Sampling and Dimension Reduction in Time Elastic Kernel Machines for Efficient Recognition of Isolated Gestures
• Exact and Approximate Minimal Pattern Mining
• Part II: Comparison of proximity measures for a topological discrimination
• Comparison of linear modularization criteria using the relational formalism, an approach to easily identify resolution limit
• A novel approach to feature selection based on quality estimation metrics
• Ultrametricity of Dissimilarity Spaces and Its Significance for Data Mining
• Part III: SMERA: Semantic Mixed Approach for Web Query Expansion and Reformulation
• Multi-layer ontologies for integrated 3D shape segmentation and annotation
• Ontology Alignment Using Web Linked Ontologies as Background Knowledge
• LIAISON: reconciLIAtion of Individuals profiles across SOcial Networks
• Clustering of Links and Clustering of Nodes: Fusion of Knowledge in Social Networks.

This book presents a collection of representative and novel work in the field of data mining, knowledge discovery, clustering and classification, based on expanded and reworked versions of a selection of the best papers originally presented in French at the EGC 2014 and EGC 2015 conferences held in Rennes (France) in January 2014 and Luxembourg in January 2015. The book is in three parts: The first four chapters discuss optimization considerations in data mining. The second part explores specific quality measures, dissimilarities and ultrametrics. The final chapters focus on semantics, ontologies and social networks. Written for PhD and MSc students, as well as researchers working in the field, it addresses both theoretical and practical aspects of knowledge discovery and management.

• Introduction
• Basic Notions
• Classical Sentential Calculus and Lukasiewicz Sentential Calculus
• MV -Algebras: Generalities
• Local MV -algebras
• Perfect MV -algebras
• The Variety Generated by Perfect MV -algebras
• Representations of Perfect MV -algebras
• The Logic of Perfect Algebras
• The Logic of Quasi True
• Perfect Pavelka Logic.

This book presents the first algebraic treatment of quasi-truth fuzzy logic and covers the algebraic foundations of many-valued logic. It offers a comprehensive account of basic techniques and reports on important results showing the pivotal role played by perfect many-valued algebras (MV-algebras). It is well known that the first-order predicate Łukasiewicz logic is not complete with respect to the canonical set of truth values. However, it is complete with respect to all linearly ordered MV -algebras. As there are no simple linearly ordered MV-algebras in this case, infinitesimal elements of an MV-algebra are allowed to be truth values. The book presents perfect algebras as an interesting subclass of local MV-algebras and provides readers with the necessary knowledge and tools for formalizing the fuzzy concept of quasi true and quasi false. All basic concepts are introduced in detail to promote a better understanding of the more complex ones. It is an advanced and inspiring reference-guide for graduate students and researchers in the field of non-classical many-valued logics.

• Intelligent Agents: Integrating Multiple Components Through a Symbolic Structure / Razvan Dinu, Tiberiu Stratulat
• An Architecture for Multi-Dimensional Temporal Abstraction Supporting Decision Making in Oil-Well Drilling / Odd Erik Gundersen, Frode Sørmo
• A New Impulse Noise Filtering Algorithm Based on a Neuro-Fuzzy Network / Yueyang Li, Haichi Luo, Jun Sun
• A Fuzzy System for Educational Tasks for Children with Reading and Writing Disabilities / Adalberto Bosco C. Pereira
• Optimizing the Performance of a Refrigeration System Using an Invasive Weed Optimization Algorithm / Roozbeh Razavi-Far, Vasile Palade, Jun Sun
• A New Cooperative Evolutionary Multi-Swarm Optimizer Algorithm Based on CUDA Architecture Applied to Engineering Optimization / Daniel Leal Souza, Otávio Noura Teixeira
• Hybrid Approach of Genetic Programming and Quantum-Behaved Particle Swarm Optimization for Modeling and Optimization of Fermentation Processes / Jun Sun, Vasile Palade, Zhenyu Wang
• Hybrid Client Specific Discriminant Analysis and its Application to Face Verification / Xiao-Qi Sun, Xiao-Jun Wu, Jun Sun.

The combination of different intelligent methods is a very active research area in Artificial Intelligence (AI). The aim is to create integrated or hybrid methods that benefit from each of their components. The 3rd Workshop on ""Combinations of Intelligent Methods and Applications"" (CIMA 2012) was intended to become a forum for exchanging experience and ideas among researchers and practitioners who are dealing with combining intelligent methods either based on first principles or in the context of specific applications. CIMA 2012 was held in conjunction with the 22nd European Conference on A.

• Chapter 1 Introduction
• Chapter 2 Analysis Process
• Chapter 3 How it Works
• Chapter 4 Goals
• Chapter 5 Visitor Analysis
• Chapter 6 Traffic Analysis
• Chapter 7 How People Use Content
• Chapter 8 Clickpath Analysis
• Chapter 9 Segmentation
• Chapter 10 Putting it Together
• Chapter 11 Testing
• Chapter 12 Measuring Behavior within Pages
• Chapter 13 AB Testing
• Chapter 14 Profiles
• Chapter 15 Culture
• Chapter 16 Conclusion.
• (source: Nielsen Book Data)

Practical Web Analytics for User Experience teaches you how to use web analytics to help answer the complicated questions facing UX professionals. Within this book, you'll find a quantitative approach for measuring a website's effectiveness and the methods for posing and answering specific questions about how users navigate a website. The book is organized according to the concerns UX practitioners face. Chapters are devoted to traffic, clickpath, and content use analysis, measuring the effectiveness of design changes, including A/B testing, building user profiles based on search habits, supporting usability test findings with reporting, and more. This is the must-have resource you need to start capitalizing on web analytics and analyze websites effectively.
(source: Nielsen Book Data)

• Title Page; Preface; Conference Organization; Contents; Invited Talks; Some Real-World Applications of Soft Artificial Intelligence: Scientogram Mining, Assembly Line Balancing, and Forensic Identification; Challenges of Automation and Safety in Field Robotics; KDD, DM and Machine Learning; Data Mining and Query Answer Techniques Applied to a Bio-Nutritional Trials Focused Expert System; The Use of the Traffic Lights Panel as a Goodness-of-Clustering Indicator: An Application to Financial Assets

• Using Gabriel Graphs in Borderline-SMOTE to Deal with Severe Two-Class Imbalance Problems on Neural NetworksActive Learning of Actions Based on Support Vector Machines; Towards the Formalization of Re-Identification for Some Data Masking Methods; Natural Language Processing and Recommenders; Towards Object Descriptions in Natural Language from Qualitative Models; Semantically-Enhanced Recommenders; Computer Vision; Supervised Texture Classification Using Optimization Techniques; Modelling Facial Expressions Dynamics with Gaussian Process Regression; Survey on 2D and 3D Human Pose Recovery

• A Study of Registration Techniques for 6DoF SLAMRobotics; Object Detection Methods for Robot Grasping: Experimental Assessment and Tuning; The Role of i-Walker in Post-Stroke Training; Using a RGB-D Camera for 6DoF SLAM; Learning Topological SLAM Using Visual Information; AI for Optimization Problems; Exploring Genetic Algorithms and Simulated Annealing for Immobile Location-Allocation Problem; Analysis and Generation of Pseudo-Industrial MaxSAT Instances; A SAT-Based Approach to MinSAT; Multicast Session Protection Planner
• Tool to Plan and Deploy Protection Infrastructure: A SPEA Approach

• AI Applications to Real WorldA Case-Based Hybrid System for Injection Molding Sensorization; First Studies on Self-Preserving Digital Objects; Intelligent Building Energy Management Through Holistic Knowledge Based Approach; Quantitative and Qualitative Approaches for Stock Movement Prediction; Subject Index; Author Index

• Part I. Density Ratio Approach to Machine Learning: 1. Introduction
• Part II. Methods of Density Ratio Estimation: 2. Density estimation
• 3. Moment matching
• 4. Probabilistic classification
• 5. Density fitting
• 6. Density-ratio fitting
• 7. Unified framework
• 8. Direct density-ratio estimation with dimensionality reduction
• Part III. Applications of Density Ratios in Machine Learning: 9. Importance sampling
• 10. Distribution comparison
• 11. Mutual information estimation
• 12. Conditional probability estimation
• Part IV. Theoretical Analysis of Density Ratio Estimation: 13. Parametric convergence analysis
• 14. Non-parametric convergence analysis
• 15. Parametric two-sample test
• 16. Non-parametric numerical stability analysis
• Part V. Conclusions: 17. Conclusions and future directions.
• (source: Nielsen Book Data)

Machine learning is an interdisciplinary field of science and engineering that studies mathematical theories and practical applications of systems that learn. This book introduces theories, methods and applications of density ratio estimation, which is a newly emerging paradigm in the machine learning community. Various machine learning problems such as non-stationarity adaptation, outlier detection, dimensionality reduction, independent component analysis, clustering, classification and conditional density estimation can be systematically solved via the estimation of probability density ratios. The authors offer a comprehensive introduction of various density ratio estimators including methods via density estimation, moment matching, probabilistic classification, density fitting and density ratio fitting, as well as describing how these can be applied to machine learning. The book provides mathematical theories for density ratio estimation including parametric and non-parametric convergence analysis and numerical stability analysis to complete the first and definitive treatment of the entire framework of density ratio estimation in machine learning.
(source: Nielsen Book Data)