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Self-Organizing Maps

The Self-Organizing Map (SOM) algorithm was introduced by the author in 1981. Its theory and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technolgies have already been based on it. The most important practical applications are in ex...

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Bibliographic Details
Main Author: Kohonen, Teuvo
Format: eBook
Language:English
Published: Berlin, Heidelberg Springer Berlin Heidelberg 1995, 1995
Edition:1st ed. 1995
Series:Springer Series in Information Sciences
Subjects:
Telecommunication
Communications Engineering, Networks
Mathematics
Biophysics
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
Table of Contents:
  • 8.2 A Fast Digital Classifier Circuit
  • 8.3 SIMD Implementation of SOM
  • 8.4 Transputer Implementation of SOM
  • 8.5 Systolic-Array Implementation of SOM
  • 8.6 The COKOS Chip
  • 8.7 The TInMANN Chip
  • 9. An Overview of SOM Literature
  • 9.1 General
  • 9.2 Early Works on Competitive Learning
  • 9.3 Status of the Mathematical Analyses
  • 9.4 Survey of General Aspects of the SOM
  • 9.5 Modifications and Analyses of LVQ
  • 9.6 Survey of Diverse Applications of SOM
  • 9.7 Applications of LVQ
  • 9.8 Survey of SOM and LVQ Implementations
  • 10. Glossary of “Neural” Terms
  • References
  • 3.3 Preliminary Demonstrations of Topology-Preserving Mappings
  • 3.4 Basic Mathematical Approaches to Self-Organization
  • 3.5 Initialization of the SOM Algorithms
  • 3.6 On the “Optimal” Learning-Rate Factor
  • 3.7 Effect of the Form of the Neighborhood Function
  • 3.8 Magnification Factor
  • 3.9 Practical Advice for the Construction of Good Maps
  • 3.10 Examples of Data Analyses Implemented by the SOM
  • 3.11 Using Gray Levels to Indicate Clusters in the SOM
  • 3.12 Derivation of the SOM Algorithm in the General Metric
  • 3.13 What Kind of SOM Actually Ensues from the Distortion Measure?
  • 3.14 Batch Computation of the SOM (“Batch Map”)
  • 4. Physiological Interpretation of SOM
  • 4.1 Two Different Lateral Control Mechanisms
  • 4.2 Learning Equation
  • 4.3 System Models of SOM and Their Simulations
  • 4.4 Recapitulation of the Features of the Physiological SOM Model.-5. Variants of SOM
  • 5.1 Overview of Ideas to Modify the Basic SOM
  • 5.2 Adaptive Tensorial Weights
  • 5.3 Tree-Structured SOM in Searching
  • 5.4 Different Definitions of the Neighborhood
  • 5.5 Neighborhoods in the Signal Space
  • 5.6 Dynamical Elements Added to the SOM
  • 5.7 Operator Maps
  • 5.8 Supervised SOM
  • 5.9 Adaptive-Subspace SOM (ASSOM) for the Implementation of Wavelets and Gabor Filters
  • 5.10 Feedback-Controlled Adaptive-Subspace SOM (FASSOM) …
  • 6. Learning Vector Quantization
  • 6.1 Optimal Decision
  • 6.2 The LVQ1
  • 6.3 The Optimized-Learning-Rate LVQ1 (OLVQ1)
  • 6.4 The LVQ2 (LVQ2.1)
  • 6.5 The LVQ3
  • 6.6 Differences Between LVQ1, LVQ2 and LVQ3
  • 6.7 General Considerations
  • 6.8 The Hypermap-Type LVQ
  • 6.9 The “LVQ-SOM”
  • 7. Applications
  • 7.1 Preprocessing
  • 7.2 Process and Machine State Monitoring
  • 7.3 Diagnosis of Speech Voicing
  • 7.4 Transcription of Continuous Speech
  • 7.5 Texture Analysis
  • 7.6 Contextual Maps
  • 7.7 Robot-Arm Control I
  • 7.8 Robot-Arm Control II
  • 8. Hardware for SOM
  • 8.1 An Analog Classifier Circuit
  • 1. Mathematical Preliminaries
  • 1.1 Mathematical Concepts and Notations
  • 1.2 Distance Measures for Patterns
  • 1.3 Statistical Pattern Recognition
  • 1.4 The Robbins-Monro Stochastic pproximation
  • 1.5 The Subspace Methods of Classification
  • 1.6 Dynamically Expanding Context
  • 2. Justification of Neural Modeling
  • 2.1 Models, Paradigms, and Methods
  • 2.2 On the Complexity of Biological Nervous Systems
  • 2.3 Relation Between Biological and Artificial Neural Networks
  • 2.4 What Functions of the Brain Are Usually Modeled?
  • 2.5 When Do We Have to Use Neural Computing?
  • 2.6 Transformation, Relaxation, and Decoder
  • 2.7 Categories of ANNs
  • 2.8 Competitive-Learning Networks
  • 2.9 Three Phases of Development of Neural Models
  • 2.10 A Simple Nonlinear Dynamic Model of the Neuron
  • 2.11 Learning Laws
  • 2.12 Brain Maps
  • 3. The Basic SOM
  • 3.1 The SOM Algorithm in the Euclidean Space
  • 3.2 The “Dot-Product SOM”

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