| Mathematical Preliminaries / 1： |
| Mathematical Concepts and Notations / 1.1： |
| Vector Space Concepts / 1.1.1： |
| Matrix Notations / 1.1.2： |
| Eigenvectors and Eigenvalues of Matrices / 1.1.3： |
| Further Properties of Matrices / 1.1.4： |
| On Matrix Differential Calculus / 1.1.5： |
| Distance Measures for Patterns / 1.2： |
| Measures of Similarity and Distance in Vector Spaces / 1.2.1： |
| Measures of Similarity and Distance Between Symbol Strings / 1.2.2： |
| Averages Over Nonvectorial Variables / 1.2.3： |
| Statistical Pattern Analysis / 1.3： |
| Basic Probabilistic Concepts / 1.3.1： |
| Projection Methods / 1.3.2： |
| Supervised Classification / 1.3.3： |
| Unsupervised Classification / 1.3.4： |
| The Subspace Methods of Classification / 1.4： |
| The Basic Subspace Method / 1.4.1： |
| Adaptation of a Model Subspace to Input Subspace / 1.4.2： |
| The Learning Subspace Method (LSM) / 1.4.3： |
| Vector Quantization / 1.5： |
| Definitions / 1.5.1： |
| Derivation of the VQ Algorithm / 1.5.2： |
| Point Density in VQ / 1.5.3： |
| Dynamically Expanding Context / 1.6： |
| Setting Up the Problem / 1.6.1： |
| Automatic Determination of Context-Independent Productions / 1.6.2： |
| Conflict Bit / 1.6.3： |
| Construction of Memory for the Context-Dependent Productions / 1.6.4： |
| The Algorithm for the Correction of New Strings / 1.6.5： |
| Estimation Procedure for Unsuccessful Searches / 1.6.6： |
| Practical Experiments / 1.6.7： |
| Neural Modeling / 2： |
| Models, Paradigms, and Methods / 2.1： |
| A History of Some Main Ideas in Neural Modeling / 2.2： |
| Issues on Artificial Intelligence / 2.3： |
| On the Complexity of Biological Nervous Systems / 2.4： |
| What the Brain Circuits Are Not / 2.5： |
| Relation Between Biological and Artificial Neural Networks / 2.6： |
| What Functions of the Brain Are Usually Modeled? / 2.7： |
| When Do We Have to Use Neural Computing? / 2.8： |
| Transformation, Relaxation, and Decoder / 2.9： |
| Categories of ANNs / 2.10： |
| A Simple Nonlinear Dynamic Model of the Neuron / 2.11： |
| Three Phases of Development of Neural Models / 2.12： |
| Learning Laws / 2.13： |
| Hebb's Law / 2.13.1： |
| The Riccati-Type Learning Law / 2.13.2： |
| The PCA-Type Learning Law / 2.13.3： |
| Some Really Hard Problems / 2.14： |
| Brain Maps / 2.15： |
| The Basic SOM / 3： |
| A Qualitative Introduction to the SOM / 3.1： |
| The Original Incremental SOM Algorithm / 3.2： |
| The "Dot-Product SOM" / 3.3： |
| Other Preliminary Demonstrations of Topology-Preserving Mappings / 3.4： |
| Ordering of Reference Vectors in the Input Space / 3.4.1： |
| Demonstrations of Ordering of Responses in the Output Space / 3.4.2： |
| Basic Mathematical Approaches to Self-Organization / 3.5： |
| One-Dimensional Case / 3.5.1： |
| Constructive Proof of Ordering of Another One-Dimensional SOM / 3.5.2： |
| The Batch Map / 3.6： |
| Initialization of the SOM Algorithms / 3.7： |
| On the "Optimal" Learning-Rate Factor / 3.8： |
| Effect of the Form of the Neighborhood Function / 3.9： |
| Does the SOM Algorithm Ensue from a Distortion Measure? / 3.10： |
| An Attempt to Optimize the SOM / 3.11： |
| Point Density of the Model Vectors / 3.12： |
| Earlier Studies / 3.12.1： |
| Numerical Check of Point Densities in a Finite One-Dimensional SOM / 3.12.2： |
| Practical Advice for the Construction of Good Maps / 3.13： |
| Examples of Data Analyses Implemented by the SOM / 3.14： |
| Attribute Maps with Full Data Matrix / 3.14.1： |
| Case Example of Attribute Maps Based on Incomplete Data Matrices (Missing Data): "Poverty Map" / 3.14.2： |
| Using Gray Levels to Indicate Clusters in the SOM / 3.15： |
| Interpretation of the SOM Mapping / 3.16： |
| "Local Principal Components" / 3.16.1： |
| Contribution of a Variable to Cluster Structures / 3.16.2： |
| Speedup of SOM Computation / 3.17： |
| Shortcut Winner Search / 3.17.1： |
| Increasing the Number of Units in the SOM / 3.17.2： |
| Smoothing / 3.17.3： |
| Combination of Smoothing, Lattice Growing, and SOM Algorithm / 3.17.4： |
| Physiological Interpretation of SOM / 4： |
| Conditions for Abstract Feature Maps in the Brain / 4.1： |
| Two Different Lateral Control Mechanisms / 4.2： |
| The WTA Function, Based on Lateral Activity Control / 4.2.1： |
| Lateral Control of Plasticity / 4.2.2： |
| Learning Equation / 4.3： |
| System Models of SOM and Their Simulations / 4.4： |
| Recapitulation of the Features of the Physiological SOM Model / 4.5： |
| Similarities Between the Brain Maps and Simulated Feature Maps / 4.6： |
| Magnification / 4.6.1： |
| Imperfect Maps / 4.6.2： |
| Overlapping Maps / 4.6.3： |
| Variants of SOM / 5： |
| Overview of Ideas to Modify the Basic SOM / 5.1： |
| Adaptive Tensorial Weights / 5.2： |
| Tree-Structured SOM in Searching / 5.3： |
| Different Definitions of the Neighborhood / 5.4： |
| Neighborhoods in the Signal Space / 5.5： |
| Dynamical Elements Added to the SOM / 5.6： |
| The SOM for Symbol Strings / 5.7： |
| Initialization of the SOM for Strings / 5.7.1： |
| The Batch Map for Strings / 5.7.2： |
| Tie-Break Rules / 5.7.3： |
| A Simple Example: The SOM of Phonemic Transcriptions / 5.7.4： |
| Operator Maps / 5.8： |
| Evolutionary-Learning SOM / 5.9： |
| Evolutionary-Learning Filters / 5.9.1： |
| Self-Organization According to a Fitness Function / 5.9.2： |
| Supervised SOM / 5.10： |
| The Adaptive-Subspace SOM (ASSOM) / 5.11： |
| The Problem of Invariant Features / 5.11.1： |
| Relation Between Invariant Features and Linear Subspaces / 5.11.2： |
| The ASSOM Algorithm / 5.11.3： |
| Derivation of the ASSOM Algorithm by Stochastic Approximation / 5.11.4： |
| ASSOM Experiments / 5.11.5： |
| Feedback-Controlled Adaptive-Subspace SOM (FASSOM) / 5.12： |
| Learning Vector Quantization / 6： |
| Optimal Decision / 6.1： |
| The LVQ1 / 6.2： |
| The Optimized-Learning-Rate LVQ1 (OLVQ1) / 6.3： |
| The Batch-LVQ1 / 6.4： |
| The Batch-LVQ1 for Symbol Strings / 6.5： |
| The LVQ2 (LVQ 2.1) / 6.6： |
| The LVQ3 / 6.7： |
| Differences Between LVQ1, LVQ2 and LVQ3 / 6.8： |
| General Considerations / 6.9： |
| The Hypermap-Type LVQ / 6.10： |
| The "LVQ-SOM" / 6.11： |
| Applications / 7： |
| Preprocessing of Optic Patterns / 7.1： |
| Blurring / 7.1.1： |
| Expansion in Terms of Global Features / 7.1.2： |
| Spectral Analysis / 7.1.3： |
| Expansion in Terms of Local Features (Wavelets) / 7.1.4： |
| Recapitulation of Features of Optic Patterns / 7.1.5： |
| Acoustic Preprocessing / 7.2： |
| Process and Machine Monitoring / 7.3： |
| Selection of Input Variables and Their Scaling / 7.3.1： |
| Analysis of Large Systems / 7.3.2： |
| Diagnosis of Speech Voicing / 7.4： |
| Transcription of Continuous Speech / 7.5： |
| Texture Analysis / 7.6： |
| Contextual Maps / 7.7： |
| Artifically Generated Clauses / 7.7.1： |
| Natural Text / 7.7.2： |
| Organization of Large Document Files / 7.8： |
| Statistical Models of Documents / 7.8.1： |
| Construction of Very Large WEBSOM Maps by the Projection Method / 7.8.2： |
| The WEBSOM of All Electronic Patent Abstracts / 7.8.3： |
| Robot-Arm Control / 7.9： |
| Simultaneous Learning of Input and Output Parameters / 7.9.1： |
| Another Simple Robot-Arm Control / 7.9.2： |
| Telecommunications / 7.10： |
| Adaptive Detector for Quantized Signals / 7.10.1： |
| Channel Equalization in the Adaptive QAM / 7.10.2： |
| Error-Tolerant Transmission of Images by a Pair of SOMs / 7.10.3： |
| The SOM as an Estimator / 7.11： |
| Symmetric (Autoassociative) Mapping / 7.11.1： |
| Asymmetric (Heteroassociative) Mapping / 7.11.2： |
| Software Tools for SOM / 8： |
| Necessary Requirements / 8.1： |
| Desirable Auxiliary Features / 8.2： |
| SOM Program Packages / 8.3： |
| SOM_PAK / 8.3.1： |
| SOM Toolbox / 8.3.2： |
| Nenet (Neural Networks Tool) / 8.3.3： |
| Viscovery SOMine / 8.3.4： |
| Examples of the Use of SOMLPAK / 8.4： |
| File Formats / 8.4.1： |
| Description of the Programs in SOM_PAK / 8.4.2： |
| A Typical Training Sequence / 8.4.3： |
| Neural-Networks Software with the SOM Option / 8.5： |
| Hardware for SOM / 9： |
| An Analog Classifier Circuit / 9.1： |
| Fast Digital Classifier Circuits / 9.2： |
| SIMD Implementation of SOM / 9.3： |
| Transputer Implementation of SOM / 9.4： |
| Systolic-Array Implementation of SOM / 9.5： |
| The COKOS Chip / 9.6： |
| The TInMANN Chip / 9.7： |
| NBISOM_25 Chip / 9.8： |
| An Overview of SOM Literature / 10： |
| Books and Review Articles / 10.1： |
| Early Works on Competitive Learning / 10.2： |
| Status of the Mathematical Analyses / 10.3： |
| Zero-Order Topology (Classical VQ) Results / 10.3.1： |
| Alternative Topological Mappings / 10.3.2： |
| Alternative Architectures / 10.3.3： |
| Functional Variants / 10.3.4： |
| Theory of the Basic SOM / 10.3.5： |
| The Learning Vector Quantization / 10.4： |
| Diverse Applications of SOM / 10.5： |
| Machine Vision and Image Analysis / 10.5.1： |
| Optical Character and Script Reading / 10.5.2： |
| Speech Analysis and Recognition / 10.5.3： |
| Acoustic and Musical Studies / 10.5.4： |
| Signal Processing and Radar Measurements / 10.5.5： |
| Industrial and Other Real-World Measurements / 10.5.6： |
| Process Control / 10.5.8： |
| Robotics / 10.5.9： |
| Electronic-Circuit Design / 10.5.10： |
| Physics / 10.5.11： |
| Chemistry / 10.5.12： |
| Biomedical Applications Without Image Processing / 10.5.13： |
| Neurophysiological Research / 10.5.14： |
| Data Processing and Analysis / 10.5.15： |
| Linguistic and AI Problems / 10.5.16： |
| Mathematical and Other Theoretical Problems / 10.5.17： |
| Applications of LVQ / 10.6： |
| Survey of SOM and LVQ Implementations / 10.7： |
| Glossary of "Neural" Terms / 11： |
| References |
| Index |
図書
