Holdings: Self-organizing maps

Self-organizing maps:

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Bibliographic Details
Main Author:	Kohonen, Teuvo (Author)
Format:	Book
Language:	English
Published:	Berlin [u.a.] Springer 2001
Edition:	3. ed.
Series:	Springer series in information sciences 30
Subjects:	Neural networks (Computer science) Self-organizing maps Selbstorganisierende Karte
Online Access:	Inhaltsverzeichnis
Item Description:	Literaturverz. S. [403] - 486
Physical Description:	XX, 501 S. Ill.
ISBN:	3540679219

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adam_text	CONTENTS 1. MATHEMATICAL PRELIMINARIES ............................... 1 1.1 MATHEMATICAL CONCEPTS AND NOTATIONS .................... 2 1.1.1 VECTOR SPACE CONCEPTS ............................ 2 1.1.2 MATRIX NOTATIONS ................................. 8 1.1.3 EIGENVECTORS AND EIGENVALUES OF MATRICES ............ 11 1.1.4 FURTHER PROPERTIES OF MATRICES ..................... 13 1.1.5 ON MATRIX DIFFERENTIAL CALCULUS .................... 15 1.2 DISTANCE MEASURES FOR PATTERNS ........................... 17 1.2.1 MEASURES OF SIMILARITY AND DISTANCE IN VECTOR SPACES . 17 1.2.2 MEASURES OF SIMILARITY AND DISTANCE BETWEEN SYMBOL STRINGS ........................... 21 1.2.3 AVERAGES OVER NONVECTORIAL VARIABLES ............... 28 1.3 STATISTICAL PATTERN ANALYSIS .............................. 29 1.3.1 BASIC PROBABILISTIC CONCEPTS ....................... 29 1.3.2 PROJECTION METHODS ............................... 34 1.3.3 SUPERVISED CLASSIFICATION .......................... 39 1.3.4 UNSUPERVISED CLASSIFICATION ........................ 44 1.4 THE SUBSPACE METHODS OF CLASSIFICATION ................... 46 1.4.1 THE BASIC SUBSPACE METHOD ....................... 46 1.4.2 ADAPTATION OF A MODEL SUBSPACE TO INPUT SUBSPACE ... 49 1.4.3 THE LEARNING SUBSPACE METHOD (LSM) .............. 53 1.5 VECTOR QUANTIZATION .................................... 59 1.5.1 DEFINITIONS ...................................... 59 1.5.2 DERIVATION OF THE VQ ALGORITHM .................... 60 1.5.3 POINT DENSITY IN VQ .............................. 62 1.6 DYNAMICALLY EXPANDING CONTEXT ......................... 64 1.6.1 SETTING UP THE PROBLEM ........................... 65 1.6.2 AUTOMATIC DETERMINATION OF CONTEXT-INDEPENDENT PRODUCTIONS ................ 66 1.6.3 CONFLICT BIT ...................................... 67 1.6.4 CONSTRUCTION OF MEMORY FOR THE CONTEXT-DEPENDENT PRODUCTIONS ............. 68 1.6.5 THE ALGORITHM FOR THE CORRECTION OF NEW STRINGS ..... 68 1.6.6 ESTIMATION PROCEDURE FOR UNSUCCESSFUL SEARCHES ...... 69 XVI CONTENTS 1.6.7 PRACTICAL EXPERIMENTS ............................ 69 2. NEURAL MODELING ......................................... 71 2.1 MODELS, PARADIGMS, AND METHODS ......................... 71 2.2 A HISTORY OF SOME MAIN IDEAS IN NEURAL MODELING .......... 72 2.3 ISSUES ON ARTIFICIAL INTELLIGENCE ........................... 75 2.4 ON THE COMPLEXITY OF BIOLOGICAL NERVOUS SYSTEMS .......... 76 2.5 WHAT THE BRAIN CIRCUITS ARE NOT ......................... 78 2.6 RELATION BETWEEN BIOLOGICAL AND ARTIFICIAL NEURAL NETWORKS ........................... 79 2.7 WHAT FUNCTIONS OF THE BRAIN ARE USUALLY MODELED? ......... 81 2.8 WHEN DO WE HAVE TO USE NEURAL COMPUTING? ............. 81 2.9 TRANSFORMATION, RELAXATION, AND DECODER .................. 82 2.10 CATEGORIES OF ANNS ..................................... 85 2.11 A SIMPLE NONLINEAR DYNAMIC MODEL OF THE NEURON ......... 87 2.12 THREE PHASES OF DEVELOPMENT OF NEURAL MODELS ............ 89 2.13 LEARNING LAWS ......................................... 91 2.13.1 HEBBS LAW ...................................... 91 2.13.2 THE RICCATI-TYPE LEARNING LAW .................... 92 2.13.3 THE PCA-TYPE LEARNING LAW ...................... 95 2.14 SOME REALLY HARD PROBLEMS ............................. 96 2.15 BRAIN MAPS ............................................ 99 3. THE BASIC SOM ........................................... 105 3.1 A QUALITATIVE INTRODUCTION TO THE SOM ................... 106 3.2 THE ORIGINAL INCREMENTAL SOM ALGORITHM ................ 109 3.3 THE DOT-PRODUCT SOM* ................................ 115 3.4 OTHER PRELIMINARY DEMONSTRATIONS OF TOPOLOGY-PRESERVING MAPPINGS ......................... 116 3.4.1 ORDERING OF REFERENCE VECTORS IN THE INPUT SPACE ..... 116 3.4.2 DEMONSTRATIONS OF ORDERING OF RESPONSES IN THE OUTPUT SPACE .............................. 120 3.5 BASIC MATHEMATICAL APPROACHES TO SELF-ORGANIZATION ........ 127 3.5.1 ONE-DIMENSIONAL CASE ............................ 128 3.5.2 CONSTRUCTIVE PROOF OF ORDERING OF ANOTHER ONE-DIMENSIONAL SOM .................. 132 3.6 THE BATCH MAP ........................................ 138 3.7 INITIALIZATION OF THE SOM ALGORITHMS ...................... 142 3.8 ON THE OPTIMAL LEARNING-RATE FACTOR ................... 143 3.9 EFFECT OF THE FORM OF THE NEIGHBORHOOD FUNCTION ........... 145 3.10 DOES THE SOM ALGORITHM ENSUE FROM A DISTORTION MEASURE? .............................. 146 3.11 AN ATTEMPT TO OPTIMIZE THE SOM ........................ 148 3.12 POINT DENSITY OF THE MODEL VECTORS ....................... 152 3.12.1 EARLIER STUDIES ................................... 152 CONTENTS XVII 3.12.2 NUMERICAL CHECK OF POINT DENSITIES IN A FINITE ONE-DIMENSIONAL SOM .................. 153 3.13 PRACTICAL ADVICE FOR THE CONSTRUCTION OF GOOD MAPS ........ 159 3.14 EXAMPLES OF DATA ANALYSES IMPLEMENTED BY THE SOM ....... 161 3.14.1 ATTRIBUTE MAPS WITH FULL DATA MATRIX .............. 161 3.14.2 CASE EXAMPLE OF ATTRIBUTE MAPS BASED ON INCOMPLETE DATA MATRICES (MISSING DATA): POVERTY MAP ................................... 165 3.15 USING GRAY LEVELS TO INDICATE CLUSTERS IN THE SOM ......... 165 3.16 INTERPRETATION OF THE SOM MAPPING ...................... 166 3.16.1 LOCAL PRINCIPAL COMPONENTS ..................... 166 3.16.2 CONTRIBUTION OF A VARIABLE TO CLUSTER STRUCTURES ...... 169 3.17 SPEEDUP OF SOM COMPUTATION ........................... 170 3.17.1 SHORTCUT WINNER SEARCH ........................... 170 3.17.2 INCREASING THE NUMBER OF UNITS IN THE SOM ......... 172 3.17.3 SMOOTHING ....................................... 175 3.17.4 COMBINATION OF SMOOTHING, LATTICE GROWING, AND SOM ALGORITHM .............................. 176 4. PHYSIOLOGICAL INTERPRETATION OF SOM ...................... 177 4.1 CONDITIONS FOR ABSTRACT FEATURE MAPS IN THE BRAIN ......... 177 4.2 TWO DIFFERENT LATERAL CONTROL MECHANISMS ................ 178 4.2.1 THE WTA FUNCTION, BASED ON LATERAL ACTIVITY CONTROL ........................ 179 4.2.2 LATERAL CONTROL OF PLASTICITY ....................... 184 4.3 LEARNING EQUATION ...................................... 185 4.4 SYSTEM MODELS OF SOM AND THEIR SIMULATIONS ............. 185 4.5 RECAPITULATION OF THE FEATURES OF THE PHYSIOLOGICAL SOM MODEL .......................... 188 4.6 SIMILARITIES BETWEEN THE BRAIN MAPS AND SIMULATED FEATURE MAPS ............................. 188 4.6.1 MAGNIFICATION .................................... 189 4.6.2 IMPERFECT MAPS .................................. 189 4.6.3 OVERLAPPING MAPS ................................ 189 5. VARIANTS OF SOM .......................................... 191 5.1 OVERVIEW OF IDEAS TO MODIFY THE BASIC SOM ............... 191 5.2 ADAPTIVE TENSORIAL WEIGHTS .............................. 194 5.3 TREE-STRUCTURED SOM IN SEARCHING ........................ 197 5.4 DIFFERENT DEFINITIONS OF THE NEIGHBORHOOD .................. 198 5.5 NEIGHBORHOODS IN THE SIGNAL SPACE ....................... 200 5.6 DYNAMICAL ELEMENTS ADDED TO THE SOM ................... 204 5.7 THE SOM FOR SYMBOL STRINGS ............................ 205 5.7.1 INITIALIZATION OF THE SOM FOR STRINGS ................ 205 5.7.2 THE BATCH MAP FOR STRINGS ........................ 206 XVIII CONTENTS 5.7.3 TIE-BREAK RULES .................................. 206 5.7.4 A SIMPLE EXAMPLE: THE SOM OF PHONEMIC TRANSCRIPTIONS ............... 207 5.8 OPERATOR MAPS ......................................... 207 5.9 EVOLUTIONARY-LEARNING SOM ............................. 211 5.9.1 EVOLUTIONARY-LEARNING FILTERS ...................... 211 5.9.2 SELF-ORGANIZATION ACCORDING TO A FITNESS FUNCTION .... 212 5.10 SUPERVISED SOM ....................................... 215 5.11 THE ADAPTIVE-SUBSPACE SOM (ASSOM) ................... 216 5.11.1 THE PROBLEM OF INVARIANT FEATURES .................. 216 5.11.2 RELATION BETWEEN INVARIANT FEATURES AND LINEAR SUBSPACES ............................. 218 5.11.3 THE ASSOM ALGORITHM ........................... 222 5.11.4 DERIVATION OF THE ASSOM ALGORITHM BY STOCHASTIC APPROXIMATION ...................... 226 5.11.5 ASSOM EXPERIMENTS ............................. 228 5.12 FEEDBACK-CONTROLLED ADAPTIVE-SUBSPACE SOM (FASSOM) ... 242 6. LEARNING VECTOR QUANTIZATION ............................. 245 6.1 OPTIMAL DECISION ....................................... 245 6.2 THE LVQ1 ............................................. 246 6.3 THE OPTIMIZED-LEARNING-RATE LVQ1 (OLVQ1) ............. 250 6.4 THE BATCH-LVQ1 ....................................... 251 6.5 THE BATCH-LVQ1 FOR SYMBOL STRINGS ...................... 252 6.6 THE LVQ2 (LVQ2.1) .................................... 252 6.7 THE LVQ3 ............................................. 253 6.8 DIFFERENCES BETWEEN LVQ1, LVQ2 AND LVQ3 ............... 254 6.9 GENERAL CONSIDERATIONS .................................. 254 6.10 THE HYPERMAP-TYPE LVQ ............................... 256 6.11 THE LVQ-SOM ....................................... 261 7. APPLICATIONS .............................................. 263 7.1 PREPROCESSING OF OPTIC PATTERNS .......................... 264 7.1.1 BLURRING ......................................... 265 7.1.2 EXPANSION IN TERMS OF GLOBAL FEATURES .............. 266 7.1.3 SPECTRAL ANALYSIS ................................. 266 7.1.4 EXPANSION IN TERMS OF LOCAL FEATURES (WAVELETS) ..... 267 7.1.5 RECAPITULATION OF FEATURES OF OPTIC PATTERNS ......... 267 7.2 ACOUSTIC PREPROCESSING .................................. 268 7.3 PROCESS AND MACHINE MONITORING ......................... 269 7.3.1 SELECTION OF INPUT VARIABLES AND THEIR SCALING ....... 269 7.3.2 ANALYSIS OF LARGE SYSTEMS ......................... 270 7.4 DIAGNOSIS OF SPEECH VOICING .............................. 274 7.5 TRANSCRIPTION OF CONTINUOUS SPEECH ....................... 274 7.6 TEXTURE ANALYSIS ....................................... 280 CONTENTS XIX 7.7 CONTEXTUAL MAPS ....................................... 281 7.7.1 ARTIFICALLY GENERATED CLAUSES ...................... 283 7.7.2 NATURAL TEXT ..................................... 285 7.8 ORGANIZATION OF LARGE DOCUMENT FILES .................... 286 7.8.1 STATISTICAL MODELS OF DOCUMENTS .................... 286 7.8.2 CONSTRUCTION OF VERY LARGE WEBSOM MAPS BY THE PROJECTION METHOD ......................... 292 7.8.3 THE WEBSOM OF ALL ELECTRONIC PATENT ABSTRACTS .... 296 7.9 ROBOT-ARM CONTROL ..................................... 299 7.9.1 SIMULTANEOUS LEARNING OF INPUT AND OUTPUT PARAMETERS ........................... 299 7.9.2 ANOTHER SIMPLE ROBOT-ARM CONTROL ................ 303 7.10 TELECOMMUNICATIONS .................................... 304 7.10.1 ADAPTIVE DETECTOR FOR QUANTIZED SIGNALS ............ 304 7.10.2 CHANNEL EQUALIZATION IN THE ADAPTIVE QAM ......... 305 7.10.3 ERROR-TOLERANT TRANSMISSION OF IMAGES BY A PAIR OF SOMS ................................ 306 7.11 THE SOM AS AN ESTIMATOR ............................... 308 7.11.1 SYMMETRIC (AUTOASSOCIATIVE)MAPPING .............. 308 7.11.2 ASYMMETRIC (HETEROASSOCIATIVE)MAPPING ............ 309 8. SOFTWARE TOOLS FOR SOM .................................. 311 8.1 NECESSARY REQUIREMENTS ................................. 311 8.2 DESIRABLE AUXILIARY FEATURES ............................. 313 8.3 SOM PROGRAM PACKAGES ................................. 315 8.3.1 SOM PAK ...................................... 315 8.3.2 SOM TOOLBOX .................................... 317 8.3.3 NENET (NEURAL NETWORKS TOOL) ..................... 318 8.3.4 VISCOVERY SOMINE ................................ 318 8.4 EXAMPLES OF THE USE OF SOM PAK ........................ 319 8.4.1 FILE FORMATS ..................................... 319 8.4.2 DESCRIPTION OF THE PROGRAMS IN SOM PAK ........... 322 8.4.3 A TYPICAL TRAINING SEQUENCE ....................... 326 8.5 NEURAL-NETWORKS SOFTWARE WITH THE SOM OPTION ........... 327 9. HARDWARE FOR SOM ....................................... 329 9.1 AN ANALOG CLASSIFIER CIRCUIT ............................. 329 9.2 FAST DIGITAL CLASSIFIER CIRCUITS ........................... 332 9.3 SIMD IMPLEMENTATION OF SOM ........................... 337 9.4 TRANSPUTER IMPLEMENTATION OF SOM ...................... 339 9.5 SYSTOLIC-ARRAY IMPLEMENTATION OF SOM .................... 341 9.6 THE COKOS CHIP ...................................... 342 9.7 THE TINMANN CHIP ................................... 342 9.8 NBISOM 25 CHIP ...................................... 344 XX CONTENTS 10. AN OVERVIEW OF SOM LITERATURE .......................... 347 10.1 BOOKS AND REVIEW ARTICLES .............................. 347 10.2 EARLY WORKS ON COMPETITIVE LEARNING ..................... 348 10.3 STATUS OF THE MATHEMATICAL ANALYSES ...................... 349 10.3.1 ZERO-ORDER TOPOLOGY (CLASSICAL VQ)RESULTS ........ 349 10.3.2 ALTERNATIVE TOPOLOGICAL MAPPINGS .................. 350 10.3.3 ALTERNATIVE ARCHITECTURES ......................... 350 10.3.4 FUNCTIONAL VARIANTS .............................. 351 10.3.5 THEORY OF THE BASIC SOM ......................... 352 10.4 THE LEARNING VECTOR QUANTIZATION ........................ 358 10.5 DIVERSE APPLICATIONS OF SOM ............................ 358 10.5.1 MACHINE VISION AND IMAGE ANALYSIS ................ 358 10.5.2 OPTICAL CHARACTER AND SCRIPT READING .............. 360 10.5.3 SPEECH ANALYSIS AND RECOGNITION .................. 360 10.5.4 ACOUSTIC AND MUSICAL STUDIES ..................... 361 10.5.5 SIGNAL PROCESSING AND RADAR MEASUREMENTS ......... 362 10.5.6 TELECOMMUNICATIONS .............................. 362 10.5.7 INDUSTRIAL AND OTHER REAL-WORLD MEASUREMENTS ..... 362 10.5.8 PROCESS CONTROL ................................. 363 10.5.9 ROBOTICS ........................................ 364 10.5.10 ELECTRONIC-CIRCUIT DESIGN ......................... 364 10.5.11 PHYSICS ......................................... 364 10.5.12 CHEMISTRY ...................................... 365 10.5.13 BIOMEDICAL APPLICATIONS WITHOUT IMAGE PROCESSING .. 365 10.5.14 NEUROPHYSIOLOGICAL RESEARCH ...................... 366 10.5.15 DATA PROCESSING AND ANALYSIS ..................... 366 10.5.16 LINGUISTIC AND AI PROBLEMS ....................... 367 10.5.17 MATHEMATICAL AND OTHER THEORETICAL PROBLEMS ...... 368 10.6 APPLICATIONS OF LVQ .................................... 369 10.7 SURVEY OF SOM AND LVQ IMPLEMENTATIONS ................. 370 11. GLOSSARY OF NEURAL TERMS .............................. 373 REFERENCES .................................................... 403 INDEX ......................................................... 487
adam_txt	CONTENTS 1. MATHEMATICAL PRELIMINARIES . 1 1.1 MATHEMATICAL CONCEPTS AND NOTATIONS . 2 1.1.1 VECTOR SPACE CONCEPTS . 2 1.1.2 MATRIX NOTATIONS . 8 1.1.3 EIGENVECTORS AND EIGENVALUES OF MATRICES . 11 1.1.4 FURTHER PROPERTIES OF MATRICES . 13 1.1.5 ON MATRIX DIFFERENTIAL CALCULUS . 15 1.2 DISTANCE MEASURES FOR PATTERNS . 17 1.2.1 MEASURES OF SIMILARITY AND DISTANCE IN VECTOR SPACES . 17 1.2.2 MEASURES OF SIMILARITY AND DISTANCE BETWEEN SYMBOL STRINGS . 21 1.2.3 AVERAGES OVER NONVECTORIAL VARIABLES . 28 1.3 STATISTICAL PATTERN ANALYSIS . 29 1.3.1 BASIC PROBABILISTIC CONCEPTS . 29 1.3.2 PROJECTION METHODS . 34 1.3.3 SUPERVISED CLASSIFICATION . 39 1.3.4 UNSUPERVISED CLASSIFICATION . 44 1.4 THE SUBSPACE METHODS OF CLASSIFICATION . 46 1.4.1 THE BASIC SUBSPACE METHOD . 46 1.4.2 ADAPTATION OF A MODEL SUBSPACE TO INPUT SUBSPACE . 49 1.4.3 THE LEARNING SUBSPACE METHOD (LSM) . 53 1.5 VECTOR QUANTIZATION . 59 1.5.1 DEFINITIONS . 59 1.5.2 DERIVATION OF THE VQ ALGORITHM . 60 1.5.3 POINT DENSITY IN VQ . 62 1.6 DYNAMICALLY EXPANDING CONTEXT . 64 1.6.1 SETTING UP THE PROBLEM . 65 1.6.2 AUTOMATIC DETERMINATION OF CONTEXT-INDEPENDENT PRODUCTIONS . 66 1.6.3 CONFLICT BIT . 67 1.6.4 CONSTRUCTION OF MEMORY FOR THE CONTEXT-DEPENDENT PRODUCTIONS . 68 1.6.5 THE ALGORITHM FOR THE CORRECTION OF NEW STRINGS . 68 1.6.6 ESTIMATION PROCEDURE FOR UNSUCCESSFUL SEARCHES . 69 XVI CONTENTS 1.6.7 PRACTICAL EXPERIMENTS . 69 2. NEURAL MODELING . 71 2.1 MODELS, PARADIGMS, AND METHODS . 71 2.2 A HISTORY OF SOME MAIN IDEAS IN NEURAL MODELING . 72 2.3 ISSUES ON ARTIFICIAL INTELLIGENCE . 75 2.4 ON THE COMPLEXITY OF BIOLOGICAL NERVOUS SYSTEMS . 76 2.5 WHAT THE BRAIN CIRCUITS ARE NOT . 78 2.6 RELATION BETWEEN BIOLOGICAL AND ARTIFICIAL NEURAL NETWORKS . 79 2.7 WHAT FUNCTIONS OF THE BRAIN ARE USUALLY MODELED? . 81 2.8 WHEN DO WE HAVE TO USE NEURAL COMPUTING? . 81 2.9 TRANSFORMATION, RELAXATION, AND DECODER . 82 2.10 CATEGORIES OF ANNS . 85 2.11 A SIMPLE NONLINEAR DYNAMIC MODEL OF THE NEURON . 87 2.12 THREE PHASES OF DEVELOPMENT OF NEURAL MODELS . 89 2.13 LEARNING LAWS . 91 2.13.1 HEBBS LAW . 91 2.13.2 THE RICCATI-TYPE LEARNING LAW . 92 2.13.3 THE PCA-TYPE LEARNING LAW . 95 2.14 SOME REALLY HARD PROBLEMS . 96 2.15 BRAIN MAPS . 99 3. THE BASIC SOM . 105 3.1 A QUALITATIVE INTRODUCTION TO THE SOM . 106 3.2 THE ORIGINAL INCREMENTAL SOM ALGORITHM . 109 3.3 THE DOT-PRODUCT SOM* . 115 3.4 OTHER PRELIMINARY DEMONSTRATIONS OF TOPOLOGY-PRESERVING MAPPINGS . 116 3.4.1 ORDERING OF REFERENCE VECTORS IN THE INPUT SPACE . 116 3.4.2 DEMONSTRATIONS OF ORDERING OF RESPONSES IN THE OUTPUT SPACE . 120 3.5 BASIC MATHEMATICAL APPROACHES TO SELF-ORGANIZATION . 127 3.5.1 ONE-DIMENSIONAL CASE . 128 3.5.2 CONSTRUCTIVE PROOF OF ORDERING OF ANOTHER ONE-DIMENSIONAL SOM . 132 3.6 THE BATCH MAP . 138 3.7 INITIALIZATION OF THE SOM ALGORITHMS . 142 3.8 ON THE OPTIMAL LEARNING-RATE FACTOR . 143 3.9 EFFECT OF THE FORM OF THE NEIGHBORHOOD FUNCTION . 145 3.10 DOES THE SOM ALGORITHM ENSUE FROM A DISTORTION MEASURE? . 146 3.11 AN ATTEMPT TO OPTIMIZE THE SOM . 148 3.12 POINT DENSITY OF THE MODEL VECTORS . 152 3.12.1 EARLIER STUDIES . 152 CONTENTS XVII 3.12.2 NUMERICAL CHECK OF POINT DENSITIES IN A FINITE ONE-DIMENSIONAL SOM . 153 3.13 PRACTICAL ADVICE FOR THE CONSTRUCTION OF GOOD MAPS . 159 3.14 EXAMPLES OF DATA ANALYSES IMPLEMENTED BY THE SOM . 161 3.14.1 ATTRIBUTE MAPS WITH FULL DATA MATRIX . 161 3.14.2 CASE EXAMPLE OF ATTRIBUTE MAPS BASED ON INCOMPLETE DATA MATRICES (MISSING DATA): POVERTY MAP . 165 3.15 USING GRAY LEVELS TO INDICATE CLUSTERS IN THE SOM . 165 3.16 INTERPRETATION OF THE SOM MAPPING . 166 3.16.1 LOCAL PRINCIPAL COMPONENTS . 166 3.16.2 CONTRIBUTION OF A VARIABLE TO CLUSTER STRUCTURES . 169 3.17 SPEEDUP OF SOM COMPUTATION . 170 3.17.1 SHORTCUT WINNER SEARCH . 170 3.17.2 INCREASING THE NUMBER OF UNITS IN THE SOM . 172 3.17.3 SMOOTHING . 175 3.17.4 COMBINATION OF SMOOTHING, LATTICE GROWING, AND SOM ALGORITHM . 176 4. PHYSIOLOGICAL INTERPRETATION OF SOM . 177 4.1 CONDITIONS FOR ABSTRACT FEATURE MAPS IN THE BRAIN . 177 4.2 TWO DIFFERENT LATERAL CONTROL MECHANISMS . 178 4.2.1 THE WTA FUNCTION, BASED ON LATERAL ACTIVITY CONTROL . 179 4.2.2 LATERAL CONTROL OF PLASTICITY . 184 4.3 LEARNING EQUATION . 185 4.4 SYSTEM MODELS OF SOM AND THEIR SIMULATIONS . 185 4.5 RECAPITULATION OF THE FEATURES OF THE PHYSIOLOGICAL SOM MODEL . 188 4.6 SIMILARITIES BETWEEN THE BRAIN MAPS AND SIMULATED FEATURE MAPS . 188 4.6.1 MAGNIFICATION . 189 4.6.2 IMPERFECT MAPS . 189 4.6.3 OVERLAPPING MAPS . 189 5. VARIANTS OF SOM . 191 5.1 OVERVIEW OF IDEAS TO MODIFY THE BASIC SOM . 191 5.2 ADAPTIVE TENSORIAL WEIGHTS . 194 5.3 TREE-STRUCTURED SOM IN SEARCHING . 197 5.4 DIFFERENT DEFINITIONS OF THE NEIGHBORHOOD . 198 5.5 NEIGHBORHOODS IN THE SIGNAL SPACE . 200 5.6 DYNAMICAL ELEMENTS ADDED TO THE SOM . 204 5.7 THE SOM FOR SYMBOL STRINGS . 205 5.7.1 INITIALIZATION OF THE SOM FOR STRINGS . 205 5.7.2 THE BATCH MAP FOR STRINGS . 206 XVIII CONTENTS 5.7.3 TIE-BREAK RULES . 206 5.7.4 A SIMPLE EXAMPLE: THE SOM OF PHONEMIC TRANSCRIPTIONS . 207 5.8 OPERATOR MAPS . 207 5.9 EVOLUTIONARY-LEARNING SOM . 211 5.9.1 EVOLUTIONARY-LEARNING FILTERS . 211 5.9.2 SELF-ORGANIZATION ACCORDING TO A FITNESS FUNCTION . 212 5.10 SUPERVISED SOM . 215 5.11 THE ADAPTIVE-SUBSPACE SOM (ASSOM) . 216 5.11.1 THE PROBLEM OF INVARIANT FEATURES . 216 5.11.2 RELATION BETWEEN INVARIANT FEATURES AND LINEAR SUBSPACES . 218 5.11.3 THE ASSOM ALGORITHM . 222 5.11.4 DERIVATION OF THE ASSOM ALGORITHM BY STOCHASTIC APPROXIMATION . 226 5.11.5 ASSOM EXPERIMENTS . 228 5.12 FEEDBACK-CONTROLLED ADAPTIVE-SUBSPACE SOM (FASSOM) . 242 6. LEARNING VECTOR QUANTIZATION . 245 6.1 OPTIMAL DECISION . 245 6.2 THE LVQ1 . 246 6.3 THE OPTIMIZED-LEARNING-RATE LVQ1 (OLVQ1) . 250 6.4 THE BATCH-LVQ1 . 251 6.5 THE BATCH-LVQ1 FOR SYMBOL STRINGS . 252 6.6 THE LVQ2 (LVQ2.1) . 252 6.7 THE LVQ3 . 253 6.8 DIFFERENCES BETWEEN LVQ1, LVQ2 AND LVQ3 . 254 6.9 GENERAL CONSIDERATIONS . 254 6.10 THE HYPERMAP-TYPE LVQ . 256 6.11 THE LVQ-SOM . 261 7. APPLICATIONS . 263 7.1 PREPROCESSING OF OPTIC PATTERNS . 264 7.1.1 BLURRING . 265 7.1.2 EXPANSION IN TERMS OF GLOBAL FEATURES . 266 7.1.3 SPECTRAL ANALYSIS . 266 7.1.4 EXPANSION IN TERMS OF LOCAL FEATURES (WAVELETS) . 267 7.1.5 RECAPITULATION OF FEATURES OF OPTIC PATTERNS . 267 7.2 ACOUSTIC PREPROCESSING . 268 7.3 PROCESS AND MACHINE MONITORING . 269 7.3.1 SELECTION OF INPUT VARIABLES AND THEIR SCALING . 269 7.3.2 ANALYSIS OF LARGE SYSTEMS . 270 7.4 DIAGNOSIS OF SPEECH VOICING . 274 7.5 TRANSCRIPTION OF CONTINUOUS SPEECH . 274 7.6 TEXTURE ANALYSIS . 280 CONTENTS XIX 7.7 CONTEXTUAL MAPS . 281 7.7.1 ARTIFICALLY GENERATED CLAUSES . 283 7.7.2 NATURAL TEXT . 285 7.8 ORGANIZATION OF LARGE DOCUMENT FILES . 286 7.8.1 STATISTICAL MODELS OF DOCUMENTS . 286 7.8.2 CONSTRUCTION OF VERY LARGE WEBSOM MAPS BY THE PROJECTION METHOD . 292 7.8.3 THE WEBSOM OF ALL ELECTRONIC PATENT ABSTRACTS . 296 7.9 ROBOT-ARM CONTROL . 299 7.9.1 SIMULTANEOUS LEARNING OF INPUT AND OUTPUT PARAMETERS . 299 7.9.2 ANOTHER SIMPLE ROBOT-ARM CONTROL . 303 7.10 TELECOMMUNICATIONS . 304 7.10.1 ADAPTIVE DETECTOR FOR QUANTIZED SIGNALS . 304 7.10.2 CHANNEL EQUALIZATION IN THE ADAPTIVE QAM . 305 7.10.3 ERROR-TOLERANT TRANSMISSION OF IMAGES BY A PAIR OF SOMS . 306 7.11 THE SOM AS AN ESTIMATOR . 308 7.11.1 SYMMETRIC (AUTOASSOCIATIVE)MAPPING . 308 7.11.2 ASYMMETRIC (HETEROASSOCIATIVE)MAPPING . 309 8. SOFTWARE TOOLS FOR SOM . 311 8.1 NECESSARY REQUIREMENTS . 311 8.2 DESIRABLE AUXILIARY FEATURES . 313 8.3 SOM PROGRAM PACKAGES . 315 8.3.1 SOM PAK . 315 8.3.2 SOM TOOLBOX . 317 8.3.3 NENET (NEURAL NETWORKS TOOL) . 318 8.3.4 VISCOVERY SOMINE . 318 8.4 EXAMPLES OF THE USE OF SOM PAK . 319 8.4.1 FILE FORMATS . 319 8.4.2 DESCRIPTION OF THE PROGRAMS IN SOM PAK . 322 8.4.3 A TYPICAL TRAINING SEQUENCE . 326 8.5 NEURAL-NETWORKS SOFTWARE WITH THE SOM OPTION . 327 9. HARDWARE FOR SOM . 329 9.1 AN ANALOG CLASSIFIER CIRCUIT . 329 9.2 FAST DIGITAL CLASSIFIER CIRCUITS . 332 9.3 SIMD IMPLEMENTATION OF SOM . 337 9.4 TRANSPUTER IMPLEMENTATION OF SOM . 339 9.5 SYSTOLIC-ARRAY IMPLEMENTATION OF SOM . 341 9.6 THE COKOS CHIP . 342 9.7 THE TINMANN CHIP . 342 9.8 NBISOM 25 CHIP . 344 XX CONTENTS 10. AN OVERVIEW OF SOM LITERATURE . 347 10.1 BOOKS AND REVIEW ARTICLES . 347 10.2 EARLY WORKS ON COMPETITIVE LEARNING . 348 10.3 STATUS OF THE MATHEMATICAL ANALYSES . 349 10.3.1 ZERO-ORDER TOPOLOGY (CLASSICAL VQ)RESULTS . 349 10.3.2 ALTERNATIVE TOPOLOGICAL MAPPINGS . 350 10.3.3 ALTERNATIVE ARCHITECTURES . 350 10.3.4 FUNCTIONAL VARIANTS . 351 10.3.5 THEORY OF THE BASIC SOM . 352 10.4 THE LEARNING VECTOR QUANTIZATION . 358 10.5 DIVERSE APPLICATIONS OF SOM . 358 10.5.1 MACHINE VISION AND IMAGE ANALYSIS . 358 10.5.2 OPTICAL CHARACTER AND SCRIPT READING . 360 10.5.3 SPEECH ANALYSIS AND RECOGNITION . 360 10.5.4 ACOUSTIC AND MUSICAL STUDIES . 361 10.5.5 SIGNAL PROCESSING AND RADAR MEASUREMENTS . 362 10.5.6 TELECOMMUNICATIONS . 362 10.5.7 INDUSTRIAL AND OTHER REAL-WORLD MEASUREMENTS . 362 10.5.8 PROCESS CONTROL . 363 10.5.9 ROBOTICS . 364 10.5.10 ELECTRONIC-CIRCUIT DESIGN . 364 10.5.11 PHYSICS . 364 10.5.12 CHEMISTRY . 365 10.5.13 BIOMEDICAL APPLICATIONS WITHOUT IMAGE PROCESSING . 365 10.5.14 NEUROPHYSIOLOGICAL RESEARCH . 366 10.5.15 DATA PROCESSING AND ANALYSIS . 366 10.5.16 LINGUISTIC AND AI PROBLEMS . 367 10.5.17 MATHEMATICAL AND OTHER THEORETICAL PROBLEMS . 368 10.6 APPLICATIONS OF LVQ . 369 10.7 SURVEY OF SOM AND LVQ IMPLEMENTATIONS . 370 11. GLOSSARY OF NEURAL TERMS . 373 REFERENCES . 403 INDEX . 487
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spelling	Kohonen, Teuvo Verfasser aut Self-organizing maps Teuvo Kohonen 3. ed. Berlin [u.a.] Springer 2001 XX, 501 S. Ill. txt rdacontent n rdamedia nc rdacarrier Springer series in information sciences 30 Physics and astronomy online library Literaturverz. S. [403] - 486 Neural networks (Computer science) Self-organizing maps Selbstorganisierende Karte (DE-588)4305302-6 gnd rswk-swf Selbstorganisierende Karte (DE-588)4305302-6 s DE-604 Springer series in information sciences 30 (DE-604)BV000008063 SWB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015222410&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis
spellingShingle	Kohonen, Teuvo Self-organizing maps Springer series in information sciences Neural networks (Computer science) Self-organizing maps Selbstorganisierende Karte (DE-588)4305302-6 gnd
subject_GND	(DE-588)4305302-6
title	Self-organizing maps
title_auth	Self-organizing maps
title_exact_search	Self-organizing maps
title_exact_search_txtP	Self-organizing maps
title_full	Self-organizing maps Teuvo Kohonen
title_fullStr	Self-organizing maps Teuvo Kohonen
title_full_unstemmed	Self-organizing maps Teuvo Kohonen
title_short	Self-organizing maps
title_sort	self organizing maps
topic	Neural networks (Computer science) Self-organizing maps Selbstorganisierende Karte (DE-588)4305302-6 gnd
topic_facet	Neural networks (Computer science) Self-organizing maps Selbstorganisierende Karte
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015222410&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
volume_link	(DE-604)BV000008063
work_keys_str_mv	AT kohonenteuvo selforganizingmaps

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