Pattern recognition: introduction, features, classifiers and principles
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| Beteiligte Personen: | , , |
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| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Berlin ; Boston
De Gruyter Oldenbourg
[2024]
|
| Ausgabe: | 2nd Edition |
| Schriftenreihe: | De Gruyter graduate
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| Schlagwörter: | |
| Links: | https://www.degruyter.com/isbn/9783111339191 https://d-nb.info/1305094484/04 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=035065532&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | XXV, 327 Seiten Illustrationen, Diagramme 24 cm x 17 cm, 588 g |
| ISBN: | 9783111339191 311133919X |
Internformat
MARC
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| 100 | 1 | |a Beyerer, Jürgen |d 1961- |e Verfasser |0 (DE-588)114116733 |4 aut | |
| 245 | 1 | 0 | |a Pattern recognition |b introduction, features, classifiers and principles |c Jürgen Beyerer, Raphael Hagmanns, Daniel Stadler |
| 250 | |a 2nd Edition | ||
| 264 | 1 | |a Berlin ; Boston |b De Gruyter Oldenbourg |c [2024] | |
| 264 | 4 | |c © 2024 | |
| 300 | |a XXV, 327 Seiten |b Illustrationen, Diagramme |c 24 cm x 17 cm, 588 g | ||
| 336 | |b txt |2 rdacontent | ||
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| 653 | |a Artificial Intelligence; Data Mining; Auotmation; Machine Learning | ||
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Datensatz im Suchindex
| _version_ | 1820959160903139328 |
|---|---|
| adam_text |
CONTENTS
PREFACE
-
V
PREFACE
OF
2ND
EDITION
-
VII
LIST
OF
TABLES
-
XIII
LIST
OF
FIGURES
-
XV
NOTATION
-
XIX
INTRODUCTION
-
XXIII
1
1.1
1.2
1.3
1.4
1.5
FUNDAMENTALS
AND
DEFINITIONS
-
1
GOALS
OF
PATTERN
RECOGNITION
-
1
STRUCTURE
OF
A
PATTERN
RECOGNITION
SYSTEM
-
2
ABSTRACT
VIEW
OF
PATTERN
RECOGNITION
-
4
DESIGN
OF
A
PATTERN
RECOGNITION
SYSTEM
-
5
EXERCISES
-
9
2
2.1
2.1.1
2.1.2
2.1.3
2.1.4
2.2
2.2.1
2.2.2
2.3
2.4
2.4.1
2.4.2
2.4.3
2.4.4
2.4.5
2.4.6
2.4.7
2.4.8
FEATURES
-
10
TYPES
OF
FEATURES
AND
THEIR
TRAITS
-
10
NOMINAL
SCALE
-
10
ORDINAL
SCALE
-
12
INTERVAL
SCALE
-
12
RATIO
SCALE
AND
ABSOLUTE
SCALE
-
13
FEATURE
SPACE
INSPECTION
-
13
PROJECTIONS
-
14
INTERSECTIONS
AND
SLICES
-
15
TRANSFORMATIONS
OF
THE
FEATURE
SPACE
-
17
MEASUREMENT
OF
DISTANCES
IN
THE
FEATURE
SPACE
-
17
BASIC
DEFINITIONS
-
19
ELEMENTARY
NORMS
AND
METRICS
-
20
A
METRIC
FOR
SETS
-
21
METRICS
ON
THE
ORDINAL
SCALE
-
23
THE
COSINE
DISTANCE
-
23
THE
KULLBACK-LEIBLER
DIVERGENCE
-
24
THE
T-DISTRIBUTED
STOCHASTIC
NEIGHBOR
EMBEDDING
-
29
TANGENTIAL
DISTANCE
MEASURE
-
31
X
-
CONTENTS
2.5
2.5.1
NORMALIZATION
-
34
ALIGNMENT,
ELIMINATION
OF
PHYSICAL
DIMENSION,
AND
LEVELING
OF
PROPORTIONS
-
35
2.5.2
2.5.3
2.5.4
2.6
2.6.1
2.6.2
2.6.3
2.7
2.7.1
2.7.2
2.7.3
2.7.4
2.7.5
2.7.6
2.7.7
2.7.8
2.8
LIGHTING
ADJUSTMENT
OF
IMAGES
-
35
DISTORTION
ADJUSTMENT
OF
IMAGES
-
39
DYNAMIC
TIME
WARPING
-
40
SELECTION
AND
CONSTRUCTION
OF
FEATURES
-
41
DESCRIPTIVE
FEATURES
-
41
MODEL-DRIVEN
FEATURES
-
45
CONSTRUCTION
OF
INVARIANT
FEATURES
-
50
DIMENSIONALITY
REDUCTION
OF
THE
FEATURE
SPACE
-
59
PRINCIPAL
COMPONENT
ANALYSIS
-
60
KERNELIZED
PRINCIPAL
COMPONENT
ANALYSIS
-
73
INDEPENDENT
COMPONENT
ANALYSIS
-
79
MULTIPLE
DISCRIMINANT
ANALYSIS
-
83
DIMENSIONALITY
REDUCTION
WITH
T-SNE
-
90
AUTOENCODER
-
91
DIMENSIONALITY
REDUCTION
BY
FEATURE
SELECTION
-
92
BAG
OF
WORDS
-
94
EXERCISES
-
99
3
3.1
3.2
3.3
3.3.1
3.3.2
3.3.3
3.3.4
3.3.5
3.3.6
3.4
3.5
BAYESIAN
DECISION
THEORY
-
103
GENERAL
CONSIDERATIONS
-
103
THE
MAXIMUM
A
POSTERIORI
CLASSIFIER
-
106
BAYESIAN
CLASSIFICATION
-
109
THE
BAYESIAN
OPTIMAL
CLASSIFIER
-
109
REFERENCE
EXAMPLE:
OPTIMAL
DECISION
REGIONS
-
114
THE
NAIVE
BAYES
CLASSIFIER
-
116
THE
MINIMAX
CLASSIFIER
-
118
NORMALLY
DISTRIBUTED
FEATURES
-
120
ARBITRARILY
DISTRIBUTED
FEATURES
-
125
GAUSSIAN
MIXTURES
-
126
EXERCISES
-
129
4
4.1
4.2
4.3
4.3.1
4.3.2
4.4
PARAMETER
ESTIMATION
-
132
MAXIMUM
LIKELIHOOD
ESTIMATION
-
140
BAYESIAN
ESTIMATION
OF
THE
CLASS-SPECIFIC
DISTRIBUTIONS
-
142
BAYESIAN
PARAMETER
ESTIMATION
-
147
LEAST
SQUARED
ESTIMATION
ERROR
-
148
CONSTANT
PENALTY
FOR
FAILURES
-
148
ADDITIONAL
REMARKS
ON
BAYESIAN
CLASSIFICATION
-
149
4.5
4.6
PARAMETER
ESTIMATION
FOR
GAUSSIAN
MIXTURES
-
150
EXERCISES
-
159
5
5.1
5.2
5.3
5.4
PARAMETER
FREE
METHODS
-
161
THE
PARZEN
WINDOW
METHOD
-
165
THE
K-NEAREST
NEIGHBOR
METHOD
-
169
K-NEAREST
NEIGHBOR
CLASSIFICATION
-
173
EXERCISES
-
179
6
6.1
6.2
6.3
GENERAL
CONSIDERATIONS
-
180
DIMENSIONALITY
OF
THE
FEATURE
SPACE
-
180
OVERFITTING
-
187
EXERCISES
-
190
7
7.1
7.1.1
7.1.2
7.2
7.3
7.4
7.5
7.6
7.6.1
7.6.2
7.6.3
7.6.4
7.6.5
7.6.6
7.6.7
7.6.8
7.7
7.7.1
7.7.2
7.7.3
7.7.4
7.7.5
7.7.6
7.8
7.9
7.9.1
SPECIAL
CLASSIFIERS
-
191
LINEAR
DISCRIMINANTS
-
191
MORE
THAN
TWO
CLASSES
-
191
NONLINEAR
SEPARATION
-
193
THE
PERCEPTRON
-
195
LINEAR
REGRESSION
-
197
ARTIFICIAL
NEURAL
NETWORKS
-
198
AUTOENCODERS
-
204
DEEP
LEARNING
-
206
HISTORICAL
DIFFICULTIES
AND
SUCCESSFUL
APPROACHES
-
207
UNSUPERVISED
PRE-TRAINING
-
208
STOCHASTIC
GRADIENT
DESCENT
-
208
RECTIFIED
LINEAR
UNITS
-
210
CONVOLUTIONAL
NEURAL
NETWORKS
-
210
RESIDUAL
NETWORKS
-
215
VARIATIONAL
AUTOENCODERS
-
216
MIXTURE
DENSITY
NETWORKS
-
217
SUPPORT
VECTOR
MACHINES
-
219
LINEAR
SEPARATION
WITH
MAXIMUM
MARGIN
-
219
DUAL
FORMULATION
-
221
NONLINEAR
MAPPING
-
223
THE
KERNEL
TRICK
-
224
NO
LINEAR
SEPARABILITY
-
228
DISCUSSION
-
230
MATCHED
FILTERS
-
231
CLASSIFICATION
OF
SEQUENCES
-
234
MARKOV
MODELS
-
235
XII
-
-
CONTENTS
7.9.2
HIDDEN
MARKOV
MODELS
-
236
7.9.3
RECURRENT
NEURAL
NETWORKS
-
246
7.9.4
TRANSFORMERS
-
249
7.9.5
GENERATIVE
ADVERSARIAL
NETWORKS
-
252
7.10
EXERCISES
-
255
8
CLASSIFICATION
WITH
NOMINAL
FEATURES
-
257
8.1
DECISION
TREES
-
257
8.1.1
DECISION
TREE
LEARNING
-
260
8.1.2
INFLUENCE
OF
THE
FEATURES
USED
-
264
8.2
RANDOM
FORESTS
-
265
8.3
STRING
MATCHING
-
269
8.4
GRAMMARS
-
270
8.5
EXERCISES
-
271
9
CLASSIFIER-INDEPENDENT
CONCEPTS
-
273
9.1
LEARNING
THEORY
-
273
9.1.1
THE
CENTRAL
PROBLEM
OF
STATISTICAL
LEARNING
-
274
9.1.2
VAPNIK-CHERVONENKIS
LEARNING
THEORY
-
274
9.2
NO-FREE-LUNCH
THEOREM
-
277
9.3
EMPIRICAL
EVALUATION
OF
CLASSIFIER
PERFORMANCE
-
278
9.3.1
RECEIVER
OPERATING
CHARACTERISTIC
-
281
9.3.2
MULTI-CLASS
SETTING
-
282
9.3.3
THEORETICAL
BOUNDS
WITH
FINITE
TEST
SETS
-
283
9.3.4
DEALING
WITH
SMALL
DATASETS
-
284
9.4
BOOSTING
-
286
9.5
REJECTION
-
288
9.6
EXERCISES
-
291
A
SOLUTIONS
TO
THE
EXERCISES
-
293
B
A
PRIMER
ON
LIE
THEORY
-
307
C
RANDOM
PROCESSES
-
311
BIBLIOGRAPHY
-
315
GLOSSARY
-
319
INDEX
-
325 |
| any_adam_object | 1 |
| author | Beyerer, Jürgen 1961- Hagmanns, Raphael Stadler, Daniel |
| author_GND | (DE-588)114116733 (DE-588)1333477430 (DE-588)1333477686 |
| author_facet | Beyerer, Jürgen 1961- Hagmanns, Raphael Stadler, Daniel |
| author_role | aut aut aut |
| author_sort | Beyerer, Jürgen 1961- |
| author_variant | j b jb r h rh d s ds |
| building | Verbundindex |
| bvnumber | BV049723185 |
| classification_rvk | ST 330 ZN 6050 |
| ctrlnum | (OCoLC)1443578185 (DE-599)DNB1305094484 |
| dewey-full | 006.4 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.4 |
| dewey-search | 006.4 |
| dewey-sort | 16.4 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Elektrotechnik / Elektronik / Nachrichtentechnik |
| edition | 2nd Edition |
| format | Book |
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| id | DE-604.BV049723185 |
| illustrated | Illustrated |
| indexdate | 2025-01-11T13:26:22Z |
| institution | BVB |
| institution_GND | (DE-588)1065492103 |
| isbn | 9783111339191 311133919X |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035065532 |
| oclc_num | 1443578185 |
| open_access_boolean | |
| owner | DE-20 DE-1050 DE-29T DE-703 |
| owner_facet | DE-20 DE-1050 DE-29T DE-703 |
| physical | XXV, 327 Seiten Illustrationen, Diagramme 24 cm x 17 cm, 588 g |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | De Gruyter Oldenbourg |
| record_format | marc |
| series2 | De Gruyter graduate |
| spelling | Beyerer, Jürgen 1961- Verfasser (DE-588)114116733 aut Pattern recognition introduction, features, classifiers and principles Jürgen Beyerer, Raphael Hagmanns, Daniel Stadler 2nd Edition Berlin ; Boston De Gruyter Oldenbourg [2024] © 2024 XXV, 327 Seiten Illustrationen, Diagramme 24 cm x 17 cm, 588 g txt rdacontent n rdamedia nc rdacarrier De Gruyter graduate Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Merkmalsextraktion (DE-588)4314440-8 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Automatische Klassifikation (DE-588)4120957-6 gnd rswk-swf Mustererkennung (DE-588)4040936-3 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Automation (DE-588)4003957-2 gnd rswk-swf Artificial Intelligence Data Mining Auotmation Machine Learning TB: Textbook Artificial Intelligence; Automation; Data Mining; Machine Learning Artificial Intelligence; Data Mining; Auotmation; Machine Learning Automation Mustererkennung (DE-588)4040936-3 s Merkmalsextraktion (DE-588)4314440-8 s Automatische Klassifikation (DE-588)4120957-6 s DE-604 Künstliche Intelligenz (DE-588)4033447-8 s Data Mining (DE-588)4428654-5 s Automation (DE-588)4003957-2 s Maschinelles Lernen (DE-588)4193754-5 s Hagmanns, Raphael Verfasser (DE-588)1333477430 aut Stadler, Daniel Verfasser (DE-588)1333477686 aut De Gruyter Oldenbourg (DE-588)1065492103 pbl Erscheint auch als Online-Ausgabe, EPUB 978-3-11-133941-2 Erscheint auch als Online-Ausgabe, PDF 978-3-11-133920-7 X:MVB https://www.degruyter.com/isbn/9783111339191 B:DE-101 application/pdf https://d-nb.info/1305094484/04 Inhaltsverzeichnis DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=035065532&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p vlb 20231006 DE-101 https://d-nb.info/provenance/plan#vlb |
| spellingShingle | Beyerer, Jürgen 1961- Hagmanns, Raphael Stadler, Daniel Pattern recognition introduction, features, classifiers and principles Künstliche Intelligenz (DE-588)4033447-8 gnd Merkmalsextraktion (DE-588)4314440-8 gnd Data Mining (DE-588)4428654-5 gnd Automatische Klassifikation (DE-588)4120957-6 gnd Mustererkennung (DE-588)4040936-3 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Automation (DE-588)4003957-2 gnd |
| subject_GND | (DE-588)4033447-8 (DE-588)4314440-8 (DE-588)4428654-5 (DE-588)4120957-6 (DE-588)4040936-3 (DE-588)4193754-5 (DE-588)4003957-2 |
| title | Pattern recognition introduction, features, classifiers and principles |
| title_auth | Pattern recognition introduction, features, classifiers and principles |
| title_exact_search | Pattern recognition introduction, features, classifiers and principles |
| title_full | Pattern recognition introduction, features, classifiers and principles Jürgen Beyerer, Raphael Hagmanns, Daniel Stadler |
| title_fullStr | Pattern recognition introduction, features, classifiers and principles Jürgen Beyerer, Raphael Hagmanns, Daniel Stadler |
| title_full_unstemmed | Pattern recognition introduction, features, classifiers and principles Jürgen Beyerer, Raphael Hagmanns, Daniel Stadler |
| title_short | Pattern recognition |
| title_sort | pattern recognition introduction features classifiers and principles |
| title_sub | introduction, features, classifiers and principles |
| topic | Künstliche Intelligenz (DE-588)4033447-8 gnd Merkmalsextraktion (DE-588)4314440-8 gnd Data Mining (DE-588)4428654-5 gnd Automatische Klassifikation (DE-588)4120957-6 gnd Mustererkennung (DE-588)4040936-3 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Automation (DE-588)4003957-2 gnd |
| topic_facet | Künstliche Intelligenz Merkmalsextraktion Data Mining Automatische Klassifikation Mustererkennung Maschinelles Lernen Automation |
| url | https://www.degruyter.com/isbn/9783111339191 https://d-nb.info/1305094484/04 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=035065532&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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