| Foreword I | p. vii |
| Foreword II | p. ix |
| Preface | p. xi |
| Abstract | p. xxi |
| Evolving Connectionist Methods | p. 1 |
| Introduction | p. 3 |
| Everything Is Evolving, but What Are the Evolving Rules? | p. 3 |
| Evolving Intelligent Systems (EIS) and Evolving Connectionist Systems (ECOS) | p. 8 |
| Biological Inspirations for EIS and ECOS | p. 11 |
| About the Book | p. 13 |
| Further Reading | p. 13 |
| Feature Selection, Model Creation, and Model Validation | p. 15 |
| Feature Selection and Feature Evaluation | p. 15 |
| Incremental Feature Selection | p. 20 |
| Machine Learning Methods - A Classification Scheme | p. 21 |
| Probability and Information Measure. Bayesian Classifiers, Hidden Markov Models. Multiple Linear Regression | p. 35 |
| Support Vector Machines (SVM) | p. 40 |
| Inductive Versus Transductive Learning and Reasoning. Global, Local, and 'Personalised' Modelling | p. 44 |
| Model Validation | p. 48 |
| Exercise | p. 49 |
| Summary and Open Problems | p. 49 |
| Further Reading | p. 51 |
| Evolving Connectionist Methods for Unsupervised Learning | p. 53 |
| Unsupervised Learning from Data. Distance Measure | p. 53 |
| Clustering | p. 57 |
| Evolving Clustering Method (ECM) | p. 61 |
| Vector Quantisation. SOM and ESOM | p. 68 |
| Prototype Learning. ART | p. 73 |
| Generic Applications of Unsupervised Learning Methods | p. 75 |
| Exercise | p. 81 |
| Summary and Open Problems | p. 81 |
| Further Reading | p. 82 |
| Evolving Connectionist Methods for Supervised Learning | p. 83 |
| Connectionist Supervised Learning Methods | p. 83 |
| Simple Evolving Connectionist Methods | p. 91 |
| Evolving Fuzzy Neural Networks (EFuNN) | p. 97 |
| Knowledge Manipulation in Evolving Fuzzy Neural Networks (EFuNNs) - Rule Insertion, Rule Extraction, Rule Aggregation | p. 109 |
| Exercise | p. 124 |
| Summary and Open Questions | p. 125 |
| Further Reading | p. 126 |
| Brain Inspired Evolving Connectionist Models | p. 127 |
| State-Based ANN | p. 127 |
| Reinforcement Learning | p. 132 |
| Evolving Spiking Neural Networks | p. 133 |
| Summary and Open Questions | p. 139 |
| Further Reading | p. 140 |
| Evolving Neuro-Fuzzy Inference Models | p. 141 |
| Knowledge-Based Neural Networks | p. 141 |
| Hybrid Neuro-Fuzzy Inference System (HyFIS) | p. 146 |
| Dynamic Evolving Neuro-Fuzzy Inference Systems (DENFIS) | p. 149 |
| Transductive Neuro-Fuzzy Inference Models | p. 161 |
| Other Evolving Fuzzy Rule-Based Connectionist Systems | p. 168 |
| Exercise | p. 175 |
| Summary and Open Problems | p. 175 |
| Further Reading | p. 175 |
| Population-Generation-Based Methods: Evolutionary Computation | p. 177 |
| A Brief Introduction to EC | p. 177 |
| Genetic Algorithms and Evolutionary Strategies | p. 179 |
| Traditional Use of EC for Learning and Optimisation in ANN | p. 183 |
| EC for Parameter and Feature Optimisation of ECOS | p. 185 |
| EC for Feature and Model Parameter Optimisation of Transductive Personalised (Nearest Neighbour) Models | p. 194 |
| Particle Swarm Intelligence | p. 198 |
| Artificial Life Systems (ALife) | p. 200 |
| Exercise | p. 201 |
| Summary and Open Questions | p. 202 |
| Further Reading | p. 202 |
| Evolving Integrated Multimodel Systems | p. 203 |
| Evolving Multimodel Systems | p. 203 |
| ECOS for Adaptive Incremental Data and Model Integration | p. 209 |
| Integrating Kernel Functions and Regression Formulas in Knowledge-Based ANN | p. 215 |
| Ensemble Learning Methods for ECOS | p. 219 |
| Integrating ECOS and Evolving Ontologies | p. 225 |
| Conclusion and Open Questions | p. 226 |
| Further Reading | p. 227 |
| Evolving Intelligent Systems | p. 229 |
| Adaptive Modelling and Knowledge Discovery in Bioinformatics | p. 231 |
| Bioinformatics: Information Growth, and Emergence of Knowledge | p. 231 |
| DNA and RNA Sequence Data Analysis and Knowledge Discovery | p. 236 |
| Gene Expression Data Analysis, Rule Extraction, and Disease Profiling | p. 242 |
| Clustering of Time-Course Gene Expression Data | p. 259 |
| Protein Structure Prediction | p. 262 |
| Gene Regulatory Networks and the System Biology Approach | p. 265 |
| Summary and Open Problems | p. 272 |
| Further Reading | p. 273 |
| Dynamic Modelling of Brain Functions and Cognitive Processes | p. 275 |
| Evolving Structures and Functions in the Brain and Their Modelling | p. 275 |
| Auditory, Visual, and Olfactory Information Processing and Their Modelling | p. 282 |
| Adaptive Modelling of Brain States Based on EEG and fMRI Data | p. 290 |
| Computational Neuro-Genetic Modelling (CNGM) | p. 295 |
| Brain-Gene Ontology | p. 299 |
| Summary and Open Problems | p. 301 |
| Further Reading | p. 302 |
| Modelling the Emergence of Acoustic Segments in Spoken Languages | p. 303 |
| Introduction to the Issues of Learning Spoken Languages | p. 303 |
| The Dilemma 'Innateness Versus Learning' or 'Nature Versus Nurture' Revisited | p. 305 |
| ECOS for Modelling the Emergence of Phones and Phonemes | p. 307 |
| Modelling Evolving Bilingual Systems | p. 316 |
| Summary and Open Problems | p. 321 |
| Further Reading | p. 323 |
| Evolving Intelligent Systems for Adaptive Speech Recognition | p. 325 |
| Introduction to Adaptive Speech Recognition | p. 325 |
| Speech Signal Analysis and Speech Feature Selection | p. 329 |
| Adaptive Phoneme-Based Speech Recognition | p. 331 |
| Adaptive Whole Word and Phrase Recognition | p. 334 |
| Adaptive, Spoken Language Human-Computer Interfaces | p. 338 |
| Exercise | p. 339 |
| Summary and Open Problems | p. 339 |
| Further Reading | p. 340 |
| Evolving Intelligent Systems for Adaptive Image Processing | p. 341 |
| Image Analysis and Feature Selection | p. 341 |
| Online Colour Quantisation | p. 344 |
| Adaptive Image Classification | p. 348 |
| Incremental Face Membership Authentication and Face Recognition | p. 350 |
| Online Video-Camera Operation Recognition | p. 353 |
| Exercise | p. 357 |
| Summary and Open Problems | p. 358 |
| Further Reading | p. 358 |
| Evolving Intelligent Systems for Adaptive Multimodal Information Processing | p. 361 |
| Multimodal Information Processing | p. 361 |
| Adaptive, Integrated, Auditory and Visual Information Processing | p. 362 |
| Adaptive Person Identification Based on Integrated Auditory and Visual Information | p. 364 |
| Person Verification Based on Auditory and Visual Information | p. 373 |
| Summary and Open Problems | p. 379 |
| Further Reading | p. 380 |
| Evolving Intelligent Systems for Robotics and Decision Support | p. 381 |
| Adaptive Learning Robots | p. 381 |
| Modelling of Evolving Financial and Socioeconomic Processes | p. 382 |
| Adaptive Environmental Risk of Event Evaluation | p. 385 |
| Summary and Open Questions | p. 390 |
| Further Reading | p. 391 |
| What Is Next: Quantum Inspired Evolving Intelligent Systems? | p. 393 |
| Why Quantum Inspired EIS? | p. 393 |
| Quantum Information Processing | p. 394 |
| Quantum Inspired Evolutionary Optimisation Techniques | p. 396 |
| Quantum Inspired Connectionist Systems | p. 398 |
| Linking Quantum to Neuro-Genetic Information Processing: Is This The Challenge For the Future? | p. 400 |
| Summary and Open Questions | p. 402 |
| Further Reading | p. 403 |
| A Sample Program in MATLAB for Time-Series Analysis | p. 405 |
| A Sample MATLAB Program to Record Speech and to Transform It into FFT Coefficients as Features | p. 407 |
| A Sample MATLAB Program for Image Analysis and Feature Extraction | p. 411 |
| Macroeconomic Data Used in Section 14.2 (Chapter 14) | p. 415 |
| References | p. 417 |
| Extended Glossary | p. 439 |
| Index | p. 453 |
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