Multidimensional particle swarm optimization for machine learning and pattern recognition:
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Heidelberg ; New York, NY ; Dordrecht ; London ; Berlin
Springer
2014
|
| Series: | Adaptation, learning, and optimization
15 |
| Subjects: | |
| Links: | http://deposit.dnb.de/cgi-bin/dokserv?id=4283876&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027000728&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Item Description: | Literaturangaben. - Hergestellt on demand |
| Physical Description: | XXVIII, 321 S. Ill., graph. Darst. 24 cm |
| ISBN: | 9783642378454 9783642378461 3642378455 |
Staff View
MARC
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Record in the Search Index
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| adam_text | CONTENTS
1 INTRODUCTION 1
1.1 OPTIMIZATION ERA 2
1.2 KEY ISSUES 4
1.3 SYNOPSIS OF THE BOOK 7
REFERENCES 10
2 OPTIMIZATION TECHNIQUES: AN OVERVIEW 13
2.1 HISTORY OF OPTIMIZATION 13
2.2 DETERMINISTIC AND ANALYTIC METHODS 29
2.2.1 GRADIENT DESCENT METHOD 29
2.2.2 NEWTON-RAPHSON METHOD 30
2.2.3 NELDER-MEAD SEARCH METHOD 32
2.3 STOCHASTIC METHODS 33
2.3.1 SIMULATED ANNEALING 33
2.3.2 STOCHASTIC APPROXIMATION 35
2.4 EVOLUTIONARY ALGORITHMS 37
2.4.1 GENETIC ALGORITHMS 37
2.4.2 DIFFERENTIAL EVOLUTION 41
REFERENCES 43
3 PARTICLE SWARM OPTIMIZATION 45
3.1 INTRODUCTION 45
3.2 BASIC PSO ALGORITHM 46
3.3 SOME PSO VARIANTS 49
3.3.1 TRIBES 51
3.3.2 MULTISWARMS 53
3.4 APPLICATIONS 55
3.4.1 NONLINEAR FUNCTION MINIMIZATION 55
3.4.2 DATA CLUSTERING 57
3.4.3 ARTIFICIAL NEURAL NETWORKS 61
3.5 PROGRAMMING REMARKS AND SOFTWARE PACKAGES 74
REFERENCES 80
IX
HTTP://D-NB.INFO/103279447X
X CONTENTS
4 MULTI-DIMENSIONAL PARTICLE SWARM OPTIMIZATION 83
4.1 THE NEED FOR MULTI-DIMENSIONALITY 83
4.2 THE BASIC IDEA 85
4.3 THE MD PSO ALGORITHM 87
4.4 PROGRAMMING REMARKS AND SOFTWARE PACKAGES 92
4.4.1 MD PSO OPERATION IN PSO_MDLIB APPLICATION 92
4.4.2 MD PSO OPERATION IN PSOTESTAPP APPLICATION 94
REFERENCES 99
5 IMPROVING GLOBAL CONVERGENCE 101
5.1 FRACTIONAL GLOBAL BEST FORMATION 102
5.1.1 THE MOTIVATION 102
5.1.2 PSO WITH FGBF 102
5.1.3 MD PSO WITH FGBF 104
5.1.4 NONLINEAR FUNCTION MINIMIZATION 104
5.2 OPTIMIZATION IN DYNAMIC ENVIRONMENTS 116
5.2.1 DYNAMIC ENVIRONMENTS: THE TEST BED 116
5.2.2 MULTISWARM PSO 117
5.2.3 FGBF FOR THE MOVING PEAK BENCHMARK FOR MPB. ... 118
5.2.4 OPTIMIZATION OVER MULTIDIMENSIONAL MPB 119
5.2.5 PERFORMANCE EVALUATION ON CONVENTIONAL MPB 120
5.2.6 PERFORMANCE EVALUATION ON MULTIDIMENSIONAL
MPB 124
5.3 WHO WILL GUIDE THE GUIDE? 128
5.3.1 SPSA OVERVIEW 130
5.3.2 SA-DRIVEN PSO AND MD PSO APPLICATIONS 131
5.3.3 APPLICATIONS TO NON-LINEAR FUNCTION MINIMIZATION . . . 134
5.4 SUMMARY AND CONCLUSIONS 141
5.5 PROGRAMMING REMARKS AND SOFTWARE PACKAGES 142
5.5.1 FGBF OPERATION IN PSO_MDLIB APPLICATION 143
5.5.2 MD PSO WITH FGBF APPLICATION OVER MPB 144
REFERENCES 147
6 DYNAMIC DATA CLUSTERING 151
6.1 DYNAMIC DATA CLUSTERING VIA MD PSO WITH FGBF 152
6.1.1 THE THEORY 152
6.1.2 RESULTS ON 2D SYNTHETIC DATASETS 155
6.1.3 SUMMARY AND CONCLUSIONS 160
6.2 DOMINANT COLOR EXTRACTION 160
6.2.1 MOTIVATION 160
6.2.2 FUZZY MODEL OVER HSV-HSL COLOR DOMAINS 163
6.2.3 DC EXTRACTION RESULTS 164
6.2.4 SUMMARY AND CONCLUSIONS 170
6.3 DYNAMIC DATA CLUSTERING VIA SA-DRIVEN MD PSO 171
CONTENTS XI
6.3.1 SA-DRIVEN MD PSO-BASED DYNAMIC CLUSTERING
IN 2D DATASETS 171
6.3.2 SUMMARY AND CONCLUSIONS 174
6.4 PROGRAMMING REMARKS AND SOFTWARE PACKAGES 176
6.4.1 FGBF OPERATION IN 2D CLUSTERING 176
6.4.2 DC EXTRACTION IN PSOTESTAPP APPLICATION 179
6.4.3 SA-DRIVEN OPERATION IN PSOTESTAPP
APPLICATION 183
REFERENCES 185
7 EVOLUTIONARY ARTIFICIAL NEURAL NETWORKS 187
7.1 SEARCH FOR THE OPTIMAL ARTIFICIAL NEURAL NETWORKS:
AN OVERVIEW 188
7.2 EVOLUTIONARY NEURAL NETWORKS BY MD PSO 190
7.2.1 PSO FOR ARTIFICIAL NEURAL NETWORKS:
THE EARLY ATTEMPTS 190
7.2.2 MD PSO-BASED EVOLUTIONARY NEURAL NETWORKS 191
7.2.3 CLASSIFICATION RESULTS ON SYNTHETIC PROBLEMS 193
7.2.4 CLASSIFICATION RESULTS ON MEDICAL DIAGNOSIS
PROBLEMS 200
7.2.5 PARAMETER SENSITIVITY AND COMPUTATIONAL
COMPLEXITY ANALYSIS 203
7.3 EVOLUTIONARY RBF CLASSIFIERS FOR POLARIMETRIC SAR IMAGES. . . 205
7.3.1 POLARIMETRIC SAR DATA PROCESSING 207
7.3.2 SAR CLASSIFICATION FRAMEWORK 209
7.3.3 POLARIMETRIC SAR CLASSIFICATION RESULTS 211
7.4 SUMMARY AND CONCLUSIONS 217
7.5 PROGRAMMING REMARKS AND SOFTWARE PACKAGES 218
REFERENCES 227
8 PERSONALIZED ECG CLASSIFICATION 231
8.1 ECG CLASSIFICATION BY EVOLUTIONARY ARTIFICIAL
NEURAL NETWORKS 233
8.1.1 INTRODUCTION AND MOTIVATION 233
8.1.2 ECG DATA PROCESSING 235
8.1.3 EXPERIMENTAL RESULTS 239
8.2 CLASSIFICATION OF HOLTER REGISTERS 244
8.2.1 THE RELATED WORK 245
8.2.2 PERSONALIZED LONG-TERM ECG CLASSIFICATION:
A SYSTEMATIC APPROACH 246
8.2.3 EXPERIMENTAL RESULTS 250
8.3 SUMMARY AND CONCLUSIONS 253
8.4 PROGRAMMING REMARKS AND SOFTWARE PACKAGES 255
REFERENCES 257
XII CONTENTS
9 IMAGE CLASSIFICATION AND RETRIEVAL BY COLLECTIVE NETWORK
OF BINARY CLASSIFIERS 259
9.1 THE ERA OF CBIR 260
9.2 CONTENT-BASED IMAGE CLASSIFICATION AND RETRIEVAL
FRAMEWORK 262
9.2.1 OVERVIEW OF THE FRAMEWORK 263
9.2.2 EVOLUTIONARY UPDATE IN THE ARCHITECTURE SPACE 264
9.2.3 THE CLASSIFIER FRAMEWORK: COLLECTIVE NETWORK
OF BINARY CLASSIFIERS 265
9.3 RESULTS AND DISCUSSIONS 270
9.3.1 DATABASE CREATION AND FEATURE EXTRACTION 271
9.3.2 CLASSIFICATION RESULTS 272
9.3.3 CBIR RESULTS 277
9.4 SUMMARY AND CONCLUSIONS 280
9.5 PROGRAMMING REMARKS AND SOFTWARE PACKAGES 281
REFERENCES 293
10 EVOLUTIONARY FEATURE SYNTHESIS 295
10.1 INTRODUCTION 295
10.2 FEATURE SYNTHESIS AND SELECTION: AN OVERVIEW 297
10.3 THE EVOLUTIONARY FEATURE SYNTHESIS FRAMEWORK 299
10.3.1 MOTIVATION 299
10.3.2 EVOLUTIONARY FEATURE SYNTHESIS FRAMEWORK 301
10.4 SIMULATION RESULTS AND DISCUSSIONS 306
10.4.1 PERFORMANCE EVALUATIONS WITH RESPECT
TO DISCRIMINATION AND CLASSIFICATION 307
10.4.2 COMPARATIVE PERFORMANCE EVALUATIONS
ON CONTENT-BASED IMAGE RETRIEVAL 309
10.5 PROGRAMMING REMARKS AND SOFTWARE PACKAGES 314
REFERENCES 321
|
| any_adam_object | 1 |
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| author_GND | (DE-588)1041660588 |
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| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.62 |
| dewey-search | 519.62 |
| dewey-sort | 3519.62 |
| dewey-tens | 510 - Mathematics |
| discipline | Informatik Mathematik |
| format | Book |
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| id | DE-604.BV041555078 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T16:49:16Z |
| institution | BVB |
| isbn | 9783642378454 9783642378461 3642378455 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027000728 |
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| owner | DE-473 DE-BY-UBG |
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| physical | XXVIII, 321 S. Ill., graph. Darst. 24 cm |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | Springer |
| record_format | marc |
| series | Adaptation, learning, and optimization |
| series2 | Adaptation, learning, and optimization |
| spellingShingle | Kiranyaz, Serkan 1972- Ince, Turker Gabbouj, Moncef Multidimensional particle swarm optimization for machine learning and pattern recognition Adaptation, learning, and optimization Maschinelles Lernen (DE-588)4193754-5 gnd Partikel-Schwarm-Optimierung (DE-588)7658941-9 gnd Mustererkennung (DE-588)4040936-3 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)7658941-9 (DE-588)4040936-3 |
| title | Multidimensional particle swarm optimization for machine learning and pattern recognition |
| title_auth | Multidimensional particle swarm optimization for machine learning and pattern recognition |
| title_exact_search | Multidimensional particle swarm optimization for machine learning and pattern recognition |
| title_full | Multidimensional particle swarm optimization for machine learning and pattern recognition Serkan Kiranyaz ; Turker Ince ; Moncef Gabbouj |
| title_fullStr | Multidimensional particle swarm optimization for machine learning and pattern recognition Serkan Kiranyaz ; Turker Ince ; Moncef Gabbouj |
| title_full_unstemmed | Multidimensional particle swarm optimization for machine learning and pattern recognition Serkan Kiranyaz ; Turker Ince ; Moncef Gabbouj |
| title_short | Multidimensional particle swarm optimization for machine learning and pattern recognition |
| title_sort | multidimensional particle swarm optimization for machine learning and pattern recognition |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd Partikel-Schwarm-Optimierung (DE-588)7658941-9 gnd Mustererkennung (DE-588)4040936-3 gnd |
| topic_facet | Maschinelles Lernen Partikel-Schwarm-Optimierung Mustererkennung |
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