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 technologies have already been based on it. The most important practical applications are in e...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | German |
| Veröffentlicht: |
Berlin u.a.
Springer
1995
|
| Schriftenreihe: | Springer series in information sciences
30 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | 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 technologies have already been based on it. The most important practical applications are in exploratory data analysis, pattern recognition, speech analysis, robotics, industrial and medical diagnostics, instrumentation, and control, and literally hundreds of other tasks. In this monograph the mathematical preliminaries, background, basic ideas, and implications are expounded in a clear, well-organized form, accessible without prior expert knowledge. Still the contents are handled with theoretical rigor. |
| Beschreibung: | Literaturverz. S. 283 - 349 |
| Beschreibung: | XV, 362 S. Ill., graph. Darst. |
| ISBN: | 3540586008 |
Internformat
MARC
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| 100 | 1 | |a Kohonen, Teuvo |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Self-organizing maps |c Teuvo Kohonen |
| 264 | 1 | |a Berlin u.a. |b Springer |c 1995 | |
| 300 | |a XV, 362 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Springer series in information sciences |v 30 | |
| 500 | |a Literaturverz. S. 283 - 349 | ||
| 520 | 3 | |a 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 technologies have already been based on it. The most important practical applications are in exploratory data analysis, pattern recognition, speech analysis, robotics, industrial and medical diagnostics, instrumentation, and control, and literally hundreds of other tasks. In this monograph the mathematical preliminaries, background, basic ideas, and implications are expounded in a clear, well-organized form, accessible without prior expert knowledge. Still the contents are handled with theoretical rigor. | |
| 650 | 7 | |a Kunstmatige intelligentie |2 gtt | |
| 650 | 7 | |a Processos estocásticos especiais |2 larpcal | |
| 650 | 7 | |a Redes neurais |2 larpcal | |
| 650 | 4 | |a Neural networks (Computer science) | |
| 650 | 4 | |a Self-organizing maps | |
| 650 | 0 | 7 | |a Selbstorganisierende Karte |0 (DE-588)4305302-6 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Selbstorganisierende Karte |0 (DE-588)4305302-6 |D s |
| 689 | 0 | |5 DE-604 | |
| 830 | 0 | |a Springer series in information sciences |v 30 |w (DE-604)BV000008063 |9 30 | |
| 856 | 4 | 2 | |m DNB Datenaustausch |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=006658570&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
Datensatz im Suchindex
| _version_ | 1805067207226425344 |
|---|---|
| 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
FURTHER
PROPERTIES
OF
MATRICES
.
11
1.1.4
ON
MATRIX
DIFFERENTIAL
CALCULUS
.
13
1.2
DISTANCE
MEASURES
FOR
PATTERNS
.
15
1.2.1
MEASURES
OF
SIMILARITY
AND
DISTANCE
IN
VECTOR
SPACES
.
15
1.2.2
MEASURES
OF
SIMILARITY
AND
DISTANCE
BETWEEN
SYMBOL
STRINGS
.
19
1.3
STATISTICAL
PATTERN
RECOGNITION
.
26
1.3.1
SUPERVISED
CLASSIFICATION
.
26
1.3.2
UNSUPERVISED
CLASSIFICATION
.
29
1.4
THE
ROBBINS-MONRO
STOCHASTIC
APPROXIMATION
.
33
1.4.1
THE
ADAPTIVE
LINEAR
ELEMENT
.
33
1.4.2
VECTOR
QUANTIZATION
.
34
1.5
THE
SUBSPACE
METHODS
OF
CLASSIFICATION
.
38
1.5.1
THE
BASIC
SUBSPACE
METHOD
.
38
1.5.2
THE
LEARNING
SUBSPACE
METHOD
(LSM)
.
39
1.6
DYNAMICALLY
EXPANDING
CONTEXT
.
44
1.6.1
SETTING
UP
THE
PROBLEM
.
44
1.6.2
AUTOMATIC
DETERMINATION
OF
CONTEXT-INDEPENDENT
PRODUCTIONS
.
46
1.6.3
CONFLICT
BIT
.
47
1.6.4
CONSTRUCTION
OF
MEMORY
FOR
THE
CONTEXT-DEPENDENT
PRODUCTIONS
.
47
1.6.5
THE
ALGORITHM
FOR
THE
CORRECTION
OF
NEW
STRINGS
.
48
1.6.6
ESTIMATION
PROCEDURE
FOR
UNSUCCESSFUL
SEARCHES
.
49
1.6.7
PRACTICAL
EXPERIMENTS
.
49
2.
JUSTIFICATION
OF
NEURAL
MODELING
.
51
2.1
MODELS,
PARADIGMS,
AND
METHODS
.
51
2.2
ON
THE
COMPLEXITY
OF
BIOLOGICAL
NERVOUS
SYSTEMS
.
53
XII
CONTENTS
2.3
RELATION
BETWEEN
BIOLOGICAL
AND
ARTIFICIAL
NEURAL
NETWORKS
.
54
2.4
WHAT
FUNCTIONS
OF
THE
BRAIN
ARE
USUALLY
MODELED?
.
56
2.5
WHEN
DO
WE
HAVE
TO
USE
NEURAL
COMPUTING?
.
56
2.6
TRANSFORMATION,
RELAXATION,
AND
DECODER
.
57
2.7
CATEGORIES
OF
ANNS
.
60
2.8
COMPETITIVE-LEARNING
NETWORKS
.
61
2.9
THREE
PHASES
OF
DEVELOPMENT
OF
NEURAL
MODELS
.
62
2.10
A
SIMPLE
NONLINEAR
DYNAMIC
MODEL
OF
THE
NEURON
.
63
2.11
LEARNING
LAWS
.
65
2.11.1
HEBB
'
S
LAW
.
66
2.11.2
THE
RICCATI-TYPE
LEARNING
LAW
.
67
2.11.3
THE
PCA-TYPE
LEARNING
LAW
.
70
2.12
BRAIN
MAPS
.
71
3.
THE
BASIC
SOM
.
77
3.1
THE
SOM
ALGORITHM
IN
THE
EUCLIDEAN
SPACE
.
78
3.2
THE
"
DOT-PRODUCT
SOM
"
.
83
3.3
PRELIMINARY
DEMONSTRATIONS
OF
TOPOLOGY-PRESERVING
MAPPINGS
.
84
3.3.1
ORDERING
OF
REFERENCE
VECTORS
IN
THE
INPUT
SPACE
.
84
3.3.2
DEMONSTRATIONS
OF
ORDERING
OF
RESPONSES
IN
THE
OUTPUT
PLANE
.
88
3.4
BASIC
MATHEMATICAL
APPROACHES
TO
SELF-ORGANIZATION
.
95
3.4.1
ONE-DIMENSIONAL
CASE
.
95
3.4.2
CONSTRUCTIVE
PROOF
OF
ORDERING
OF
ANOTHER
ONE-DIMENSIONAL
SOM
.
100
3.4.3
AN
ATTEMPT
TO
JUSTIFY
THE
SOM
ALGORITHM
FOR
GENERAL
DIMENSIONALITIES
.
105
3.5
INITIALIZATION
OF
THE
SOM
ALGORITHMS
.
106
3.6
ON
THE
"
OPTIMAL
"
LEARNING-RATE
FACTOR
.
107
3.7
EFFECT
OF
THE
FORM
OF
THE
NEIGHBORHOOD
FUNCTION
.
110
3.8
MAGNIFICATION
FACTOR
.
110
3.9
PRACTICAL
ADVICE
FOR
THE
CONSTRUCTION
OF
GOOD
MAPS
.
112
3.10
EXAMPLES
OF
DATA
ANALYSES
IMPLEMENTED
BY
THE
SOM
.
113
3.10.1
ATTRIBUTE
MAPS
WITH
PULL
DATA
MATRIX
.
113
3.10.2
CASE
EXAMPLE
OF
ATTRIBUTE
MAPS
BASED
ON
INCOMPLETE
DATA
MATRICES
(MISSING
DATA):
"
POVERTY
MAP
"
.
117
3.11
USING
GRAY
LEVELS
TO
INDICATE
CLUSTERS
IN
THE
SOM
.
117
3.12
DERIVATION
OF
THE
SOM
ALGORITHM
IN
THE
GENERAL
METRIC
.
118
CONTENTS
XIII
3.13
WHAT
KIND
OF
SOM
ACTUALLY
ENSUES
FROM
THE
DISTORTION
MEASURE?
.
122
3.14
BATCH
COMPUTATION
OF
THE
SOM
(
"
BATCH
MAP
"
)
.
127
4.
PHYSIOLOGICAL
INTERPRETATION
OF
SOM
.
131
4.1
TWO
DIFFERENT
LATERAL
CONTROL
MECHANISMS
.
131
4.1.1
THE
WTA
FUNCTION,
BASED
ON
LATERAL
ACTIVITY
CONTROL
.
132
4.1.2
LATERAL
CONTROL
OF
PLASTICITY
.
137
4.2
LEARNING
EQUATION
.
138
4.3
SYSTEM
MODELS
OF
SOM
AND
THEIR
SIMULATIONS
.
138
4.4
RECAPITULATION
OF
THE
FEATURES
OF
THE
PHYSIOLOGICAL
SOM
MODEL
.
141
5.
VARIANTS
OF
SOM
.
143
5.1
OVERVIEW
OF
IDEAS
TO
MODIFY
THE
BASIC
SOM
.
143
5.2
ADAPTIVE
TENSORIAL
WEIGHTS
.
146
5.3
TREE-STRUCTURED
SOM
IN
SEARCHING
.
149
5.4
DIFFERENT
DEFINITIONS
OF
THE
NEIGHBORHOOD
.
150
5.5
NEIGHBORHOODS
IN
THE
SIGNAL
SPACE
.
152
5.6
DYNAMICAL
ELEMENTS
ADDED
TO
THE
SOM
.
156
5.7
OPERATOR
MAPS
.
157
5.8
SUPERVISED
SOM
.
160
5.9
ADAPTIVE-SUBSPACE
SOM
(ASSOM)
FOR
THE
IMPLEMENTATION
OF
WAVELETS
AND
GABOR
FILTERS
.
161
5.10
FEEDBACK-CONTROLLED
ADAPTIVE-SUBSPACE
SOM
(FASSOM)
.
170
6.
LEARNING
VECTOR
QUANTIZATION
.
175
6.1
OPTIMAL
DECISION
.
175
6.2
THE
LVQ1
.
176
6.3
THE
OPTIMIZED-LEARNING-RATE
LVQ1
(OLVQ1)
.
180
6.4
THE
LVQ2
(LVQ2.1)
.
181
6.5
THE
LVQ3
.
181
6.6
DIFFERENCES
BETWEEN
LVQ1,
LVQ2
AND
LVQ3
.
182
6.7
GENERAL
CONSIDERATIONS
.
182
6.8
THE
HYPERMAP-TYPE
LVQ
.
184
6.9
THE
"
LVQ-SOM
"
.
189
7.
APPLICATIONS
.
191
7.1
PREPROCESSING
.
192
7.2
PROCESS
AND
MACHINE
STATE
MONITORING
.
195
7.3
DIAGNOSIS
OF
SPEECH
VOICING
.
196
7.4
TRANSCRIPTION
OF
CONTINUOUS
SPEECH
.
198
7.5
TEXTURE
ANALYSIS
.
202
7.6
CONTEXTUAL
MAPS
.
204
XIV
CONTENTS
7.6.1
ROLE-BASED
SEMANTIC
MAP
.
206
7.6.2
UNSUPERVISED
CATEGORIZATION
OF
PHONEMIC
CLASSES
FROM
TEXT
.
208
7.7
ROBOT-ARM
CONTROL
I
.
209
7.8
ROBOT-ARM
CONTROL
II
.
213
8.
HARDWARE
FOR
SOM
.
215
8.1
AN
ANALOG
CLASSIFIER
CIRCUIT
.
215
8.2
A
FAST
DIGITAL
CLASSIFIER
CIRCUIT
.
218
8.3
SIMD
IMPLEMENTATION
OF
SOM
.
222
8.4
TRANSPUTER
IMPLEMENTATION
OF
SOM
.
225
8.5
SYSTOLIC-ARRAY
IMPLEMENTATION
OF
SOM
.
227
8.6
THE
COKOS
CHIP
.
228
8.7
THE
TINMANN
CHIP
.
228
9.
AN
OVERVIEW
OF
SOM
LITERATURE
.
231
9.1
GENERAL
.
231
9.2
EARLY
WORKS
ON
COMPETITIVE
LEARNING
.
233
9.3
.
STATUS
OF
THE
MATHEMATICAL
ANALYSES
.
234
9.4
^SURVEY
OF
GENERAL
ASPECTS
OF
THE
SOM
.
238
9.4.1
GENERAL
.
238
9.4.2
MATHEMATICAL
DERIVATIONS,
ANALYSES,
AND
MODIFICATIONS
OF
THE
SOM
.
238
9.5
MODIFICATIONS
AND
ANALYSES
OF
LVQ
.
240
9.6
SURVEY
OF
DIVERSE
APPLICATIONS
OF
SOM
.
241
9.6.1
MACHINE
VISION
AND
IMAGE
ANALYSIS
.
241
9.6.2
OPTICAL
CHARACTER
AND
SCRIPT
READING
.
242
9.6.3
SPEECH
ANALYSIS
AND
RECOGNITION
.
242
9.6.4
ACOUSTIC
AND
MUSICAL
STUDIES
.
243
9.6.5
SIGNAL
PROCESSING
AND
RADAR
MEASUREMENTS
.
244
9.6.6
TELECOMMUNICATIONS
.
244
9.6.7
INDUSTRIAL
AND
OTHER
REAL-WORLD
MEASUREMENTS
.
244
9.6.8
PROCESS
CONTROL
.
244
9.6.9
ROBOTICS
.
245
9.6.10
CHEMISTRY
.
245
9.6.11
PHYSICS
.
246
9.6.12
ELECTRONIC-CIRCUIT
DESIGN
.
246
9.6.13
MEDICAL
APPLICATIONS
WITHOUT
IMAGE
PROCESSING
.
246
9.6.14
DATA
PROCESSING
.
247
9.6.15
LINGUISTIC
AND
AI
PROBLEMS
.
247
9.6.16
MATHEMATICAL
PROBLEMS
.
248
9.6.17
NEUROPHYSIOLOGICAL
RESEARCH
.
249
9.7
APPLICATIONS
OF
LVQ
.
249
9.8
SURVEY
OF
SOM
AND
LVQ
IMPLEMENTATIONS
.
251
CONTENTS
XV
10.
GLOSSARY
OF
"
NEURAL
"
TERMS
.
253
REFERENCES
.
283
INDEX
.
351 |
| any_adam_object | 1 |
| author | Kohonen, Teuvo |
| author_facet | Kohonen, Teuvo |
| author_role | aut |
| author_sort | Kohonen, Teuvo |
| author_variant | t k tk |
| building | Verbundindex |
| bvnumber | BV010039679 |
| callnumber-first | Q - Science |
| callnumber-label | QA76 |
| callnumber-raw | QA76.87 |
| callnumber-search | QA76.87 |
| callnumber-sort | QA 276.87 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | ST 152 ST 300 ST 330 |
| classification_tum | DAT 717f |
| ctrlnum | (OCoLC)32012438 (DE-599)BVBBV010039679 |
| dewey-full | 006.4/2 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.4/2 |
| dewey-search | 006.4/2 |
| dewey-sort | 16.4 12 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Book |
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| id | DE-604.BV010039679 |
| illustrated | Illustrated |
| indexdate | 2024-07-20T03:30:32Z |
| institution | BVB |
| isbn | 3540586008 |
| language | German |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-006658570 |
| oclc_num | 32012438 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-N2 DE-91 DE-BY-TUM DE-92 DE-83 DE-11 |
| owner_facet | DE-355 DE-BY-UBR DE-N2 DE-91 DE-BY-TUM DE-92 DE-83 DE-11 |
| physical | XV, 362 S. Ill., graph. Darst. |
| publishDate | 1995 |
| publishDateSearch | 1995 |
| publishDateSort | 1995 |
| publisher | Springer |
| record_format | marc |
| series | Springer series in information sciences |
| series2 | Springer series in information sciences |
| spelling | Kohonen, Teuvo Verfasser aut Self-organizing maps Teuvo Kohonen Berlin u.a. Springer 1995 XV, 362 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Springer series in information sciences 30 Literaturverz. S. 283 - 349 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 technologies have already been based on it. The most important practical applications are in exploratory data analysis, pattern recognition, speech analysis, robotics, industrial and medical diagnostics, instrumentation, and control, and literally hundreds of other tasks. In this monograph the mathematical preliminaries, background, basic ideas, and implications are expounded in a clear, well-organized form, accessible without prior expert knowledge. Still the contents are handled with theoretical rigor. Kunstmatige intelligentie gtt Processos estocásticos especiais larpcal Redes neurais larpcal 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 30 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=006658570&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Kohonen, Teuvo Self-organizing maps Springer series in information sciences Kunstmatige intelligentie gtt Processos estocásticos especiais larpcal Redes neurais larpcal 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_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 | Kunstmatige intelligentie gtt Processos estocásticos especiais larpcal Redes neurais larpcal Neural networks (Computer science) Self-organizing maps Selbstorganisierende Karte (DE-588)4305302-6 gnd |
| topic_facet | Kunstmatige intelligentie Processos estocásticos especiais Redes neurais 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=006658570&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV000008063 |
| work_keys_str_mv | AT kohonenteuvo selforganizingmaps |