Evolving Connectionist Systems

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Evolving Connectionist Systems / Nikola K. Kasabov

資料種別:: 電子ブック
出版情報:: Springer eBooks Computer Science
Springer London, 2007
著者名:: Kasabov, Nikola K.
ISBN:: 9781846283451 [1846283450]
9786611339715 [661133971X]
書誌ID:: OB00350277

子書誌情報

フルテキスト

所蔵情報

書誌詳細

その他の標題:

EVOLVING CONNECTIONIST SYSTEMS

主題:

Information Technology - Artificial Intelligence

言語:

英語

目次情報:

Foreword I / Walter J. Freeman

Foreword II / John G. Taylor

Preface

Abstract

Evolving Connectionist Methods / Part I：

Introduction

Everything Is Evolving, but What Are the Evolving Rules? / I.1：

Evolving Intelligent Systems (EIS) and Evolving Connectionist Systems (ECOS) / I.2：

Biological Inspirations for EIS and ECOS / I.3：

About the Book / I.4：

Further Reading / I.5：

Feature Selection, Model Creation, and Model Validation / 1：

Feature Selection and Feature Evaluation / 1.1：

Incremental Feature Selection / 1.2：

Machine Learning Methods - A Classification Scheme / 1.3：

Probability and Information Measure. Bayesian Classifiers, Hidden Markov Models. Multiple Linear Regression / 1.4：

Support Vector Machines (SVM) / 1.5：

Inductive Versus Transductive Learning and Reasoning. Global, Local, and 'Personalised' Modelling / 1.6：

Model Validation / 1.7：

Exercise / 1.8：

Summary and Open Problems / 1.9：

Evolving Connectionist Methods for Unsupervised Learning / 1.10：

Unsupervised Learning from Data. Distance Measure / 2.1：

Clustering / 2.2：

Evolving Clustering Method (ECM) / 2.3：

Vector Quantisation. SOM and ESOM / 2.4：

Prototype Learning. ART / 2.5：

Generic Applications of Unsupervised Learning Methods / 2.6：

Evolving Connectionist Methods for Supervised Learning / 2.7：

Connectionist Supervised Learning Methods / 3.1：

Simple Evolving Connectionist Methods / 3.2：

Evolving Fuzzy Neural Networks (EFuNN) / 3.3：

Knowledge Manipulation in Evolving Fuzzy Neural Networks (EFuNNs) - Rule Insertion, Rule Extraction, Rule Aggregation / 3.4：

Summary and Open Questions / 3.5：

Brain Inspired Evolving Connectionist Models / 3.7：

State-Based ANN / 4.1：

Reinforcement Learning / 4.2：

Evolving Spiking Neural Networks / 4.3：

Evolving Neuro-Fuzzy Inference Models / 4.4：

Knowledge-Based Neural Networks / 5.1：

Hybrid Neuro-Fuzzy Inference System (HyFIS) / 5.2：

Dynamic Evolving Neuro-Fuzzy Inference Systems (DENFIS) / 5.3：

Transductive Neuro-Fuzzy Inference Models / 5.4：

Other Evolving Fuzzy Rule-Based Connectionist Systems / 5.5：

Population-Generation-Based Methods: Evolutionary Computation / 5.6：

A Brief Introduction to EC / 6.1：

Genetic Algorithms and Evolutionary Strategies / 6.2：

Traditional Use of EC for Learning and Optimisation in ANN / 6.3：

EC for Parameter and Feature Optimisation of ECOS / 6.4：

EC for Feature and Model Parameter Optimisation of Transductive Personalised (Nearest Neighbour) Models / 6.5：

Particle Swarm Intelligence / 6.6：

Artificial Life Systems (ALife) / 6.7：

Evolving Integrated Multimodel Systems / 6.8：

Evolving Multimodel Systems / 7.1：

ECOS for Adaptive Incremental Data and Model Integration / 7.2：

Integrating Kernel Functions and Regression Formulas in Knowledge-Based ANN / 7.3：

Ensemble Learning Methods for ECOS / 7.4：

Integrating ECOS and Evolving Ontologies / 7.5：

Conclusion and Open Questions / 7.6：

Evolving Intelligent Systems / 7.7：

Adaptive Modelling and Knowledge Discovery in Bioinformatics / 8：

Bioinformatics: Information Growth, and Emergence of Knowledge / 8.1：

DNA and RNA Sequence Data Analysis and Knowledge Discovery / 8.2：

Gene Expression Data Analysis, Rule Extraction, and Disease Profiling / 8.3：

Clustering of Time-Course Gene Expression Data / 8.4：

Protein Structure Prediction / 8.5：

Gene Regulatory Networks and the System Biology Approach / 8.6：

Dynamic Modelling of Brain Functions and Cognitive Processes / 8.7：

Evolving Structures and Functions in the Brain and Their Modelling / 9.1：

Auditory, Visual, and Olfactory Information Processing and Their Modelling / 9.2：

Adaptive Modelling of Brain States Based on EEG and fMRI Data / 9.3：

Computational Neuro-Genetic Modelling (CNGM) / 9.4：

Brain-Gene Ontology / 9.5：

Modelling the Emergence of Acoustic Segments in Spoken Languages / 9.6：

Introduction to the Issues of Learning Spoken Languages / 10.1：

The Dilemma 'Innateness Versus Learning' or 'Nature Versus Nurture' Revisited / 10.2：

ECOS for Modelling the Emergence of Phones and Phonemes / 10.3：

Modelling Evolving Bilingual Systems / 10.4：

Evolving Intelligent Systems for Adaptive Speech Recognition / 10.5：

Introduction to Adaptive Speech Recognition / 11.1：

Speech Signal Analysis and Speech Feature Selection / 11.2：

Adaptive Phoneme-Based Speech Recognition / 11.3：

Adaptive Whole Word and Phrase Recognition / 11.4：

Adaptive, Spoken Language Human-Computer Interfaces / 11.5：

Evolving Intelligent Systems for Adaptive Image Processing / 11.6：

Image Analysis and Feature Selection / 12.1：

Online Colour Quantisation / 12.2：

Adaptive Image Classification / 12.3：

Incremental Face Membership Authentication and Face Recognition / 12.4：

Online Video-Camera Operation Recognition / 12.5：

Evolving Intelligent Systems for Adaptive Multimodal Information Processing / 12.6：

Multimodal Information Processing / 13.1：

Adaptive, Integrated, Auditory and Visual Information Processing / 13.2：

Adaptive Person Identification Based on Integrated Auditory and Visual Information / 13.3：

Person Verification Based on Auditory and Visual Information / 13.4：

Evolving Intelligent Systems for Robotics and Decision Support / 13.5：

Adaptive Learning Robots / 14.1：

Modelling of Evolving Financial and Socioeconomic Processes / 14.2：

Adaptive Environmental Risk of Event Evaluation / 14.3：

What Is Next: Quantum Inspired Evolving Intelligent Systems? / 14.4：

Why Quantum Inspired EIS? / 15.1：

Quantum Information Processing / 15.2：

Quantum Inspired Evolutionary Optimisation Techniques / 15.3：

Quantum Inspired Connectionist Systems / 15.4：

Linking Quantum to Neuro-Genetic Information Processing: Is This The Challenge For the Future? / 15.5：

A Sample Program in MATLAB for Time-Series Analysis / 15.6：

A Sample MATLAB Program to Record Speech and to Transform It into FFT Coefficients as Features / Appendix B：

A Sample MATLAB Program for Image Analysis and Feature Extraction / Appendix C：

Macroeconomic Data Used in Section 14.2 (Chapter 14) / Appendix D：

References

Extended Glossary

Index

Foreword I / Walter J. Freeman

Foreword II / John G. Taylor

Preface

Abstract

Evolving Connectionist Methods / Part I：

Introduction

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