Ensembles in machine learning applications:
Saved in:
| Other Authors: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Berlin ; Heidelberg
Springer
2011
|
| Series: | Studies in computational intelligence
373 |
| Subjects: | |
| Online Access: | Inhaltstext Inhaltsverzeichnis |
| Item Description: | Literaturangaben |
| Physical Description: | XX, 252 S. Ill., graph. Darst. 24 cm |
| ISBN: | 9783642229091 3642229093 |
Staff View
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IMAGE 1
CONTENTS
1 FACIAL ACTION UNIT RECOGNITION USING FILTERED LOCAL BINARY PATTERN
FEATURES WITH BOOTSTRAPPED AND WEIGHTED ECOC CLASSIFIERS 1 RAYMOND S.
SMITH, TERRY WINDEATT 1.1 INTRODUCTION 1
1.2 THEORETICAL BACKGROUND 5
1.2.1 ECOC WEIGHTED DECODING 5
1.2.2 PLATT SCALING 6
1.2.3 LOCAL BINARY PATTERNS 7
1.2.4 FAST CORRELATION-BASED FILTERING 8
1.2.5 PRINCIPAL COMPONENTS ANALYSIS 9
1.3 ALGORITHMS 10
1.4 EXPERIMENTAL EVALUATION 10
1.4.1 CLASSIFIER ACCURACY 13
1.4.2 THE EFFECT OF PLATT SCALING 14
1.4.3 A BIAS/VARIANCE ANALYSIS 15
1.5 CONCLUSION 16
1.6 CODE LISTINGS 17
REFERENCES 19
2 ON THE DESIGN OF LOW REDUNDANCY ERROR-CORRECTING OUTPUT CODES 21
MIGUEL ANGEL BAUTISTA, SERGIO ESCALERA, XAVIER BARO, ORIOL PUJOL, JORDI
VITRIA, PETIA RADEVA 2. 1 INTRODUCTION 21
2.2 COMPACT ERROR-CORRECTING OUTPUT CODES 23
2.2.1 ERROR-CORRECTING OUTPUT CODES 23
2.2.2 COMPACT ECOC CODING 24
2.3 RESULTS 29
2.3.1 UCI CATEGORIZATION 30
2.3.2 COMPUTER VISION APPLICATIONS 32
BIBLIOGRAFISCHE INFORMATIONEN HTTP://D-NB.INFO/1013327136
DIGITALISIERT DURCH
IMAGE 2
XII CONTENTS
2.4 CONCLUSION 36
REFERENCES 37
3 MINIMALLY-SIZED BALANCED DECOMPOSITION SCHEMES FOR MULTI-CLASS
CLASSIFICATION 39
EVGUENI N. SMIRNOV, MATTHIJS MOED, GEORGI NALBANTOV, IDA
SPRINKHUIZEN-KUYPER 3.1 INTRODUCTION 40
3.2 CLASSIFICATION PROBLEM 41
3.3 DECOMPOSING MULTI-CLASS CLASSIFICATION PROBLEMS 41 3.3.1
DECOMPOSITION SCHEMES 41
3.3.2 ENCODING AND DECODING 44
3.4 BALANCED DECOMPOSITION SCHEMES AND THEIR MINIMALLY-SIZED VARIANT 46
3.4.1 BALANCED DECOMPOSITION SCHEMES 46
3.4.2 MINIMALLY-SIZED BALANCED DECOMPOSITION SCHEMES 47 3.4.3 VOTING
USING MINIMALLY-SIZED BALANCED DECOMPOSITION SCHEMES 49
3.5 EXPERIMENTS 51
3.5.1 UCI DATA EXPERIMENTS 51
3.5.2 EXPERIMENTS ON DATA SETS WITH LARGE NUMBER OF CLASSES 52
3.5.3 BIAS-VARIANCE DECOMPOSITION EXPERIMENTS 54 3.6 CONCLUSION 55
REFERENCES 56
4 BIAS-VARIANCE ANALYSIS OF ECOC AND BAGGING USING NEURAL NETS . . 59
CEMRE ZOR, TERRY WINDEATT, BERRIN YANIKOGLU 4.1 INTRODUCTION 59
4.1.1 BOOTSTRAP AGGREGATING (BAGGING) 60
4.1.2 ERROR CORRECTING OUTPUT CODING (ECOC) 60
4.1.3 BIAS AND VARIANCE ANALYSIS 62
4.2 BIAS AND VARIANCE ANALYSIS OF JAMES 64
4.3 EXPERIMENTS 65
4.3.1 SETUP 65
4.3.2 RESULTS 68
4.4 DISCUSSION 72
REFERENCES 72
5 FAST-ENSEMBLES OF MINIMUM REDUNDANCY FEATURE SELECTION 75 BENJAMIN
SCHOWE, KATHARINA MORIK 5. 1 INTRODUCTION 75
5.2 RELATED WORK 76
5.2.1 ENSEMBLE METHODS 78
5.3 SPEEDING UP ENSEMBLES 78
5.3.1 INNER ENSEMBLE 79
IMAGE 3
CONTENTS XIII
5.3.2 FAST ENSEMBLE 80
5.3.3 RESULT COMBINATION 84
5.3.4 BENEFITS 85
5.4 EVALUATION 85
5.4.1 STABILITY 86
5.4.2 ACCURACY 87
5.4.3 RUNTIME 92
5.4.4 LUCAS 93
5.5 CONCLUSION 94
REFERENCES 95
6 HYBRID CORRELATION AND CAUSAL FEATURE SELECTION FOR ENSEMBLE
CLASSIFIERS 97
RAKKRIT DUANGSOITHONG, TERRY WINDEATT 6.1 INTRODUCTION 97
6.2 RELATED RESEARCH 99
6.3 THEORETICAL APPROACH 100
6.3.1 FEATURE SELECTION ALGORITHMS 100
6.3.2 CAUSAL DISCOVERY ALGORITHM 102
6.3.3 FEATURE SELECTION ANALYSIS 103
6.3.4 ENSEMBLE CLASSIFIER 106
6.3.5 PSEUDO-CODE: HYBRID CORRELATION AND CAUSAL FEATURE SELECTION FOR
ENSEMBLE CLASSIFIERS ALGORITHM 106 6.4 EXPERIMENTAL SETUP 108
6.4.1 DATASET 108
6.4.2 EVALUATION 109
6.5 EXPERIMENTAL RESULT 110
6.6 DISCUSSION 113
6.7 CONCLUSION 114
REFERENCES 114
7 LEARNING MARKOV BLANKETS FOR CONTINUOUS OR DISCRETE NETWORKS VIA
FEATURE SELECTION 117
HOUTAO DENG, SAYLISSE DAVILA, GEORGE RUNGER, EUGENE TUV 7.1 INTRODUCTION
117
7.1.1 LEARNING BAYESIAN NETWORKS VIA FEATURE SELECTION 118 7.2 FEATURE
SELECTION FRAMEWORK 119
7.2.1 FEATURE IMPORTANCE MEASURE 120
7.2.2 FEATURE MASKING MEASURE AND ITS RELATIONSHIP TO MARKOV BLANKET 121
7.2.3 STATISTICAL CRITERIA FOR IDENTIFYING RELEVANT AND REDUNDANT
FEATURES 124
7.2.4 RESIDUALS FOR MULTIPLE ITERATIONS 124
7.3 EXPERIMENTS 125
7.3.1 CONTINUOUS GAUSSIAN LOCAL STRUCTURE LEARNING 125 7.3.2 CONTINUOUS
NON-GAUSSIAN LOCAL STRUCTURE LEARNING 127
IMAGE 4
XIV CONTENTS
7.3.3 DISCRETE LOCAL STRUCTURE LEARNING 128
7.4 CONCLUSION 130
REFERENCES 130
8 ENSEMBLES OF BAYESIAN NETWORK CLASSIFIERS USING GLAUCOMA DATA AND
EXPERTISE 133
STEFANO CECCON, DAVID GARWAY-HEATH, DAVID CRABB, ALLAN TUCKER 8.1
IMPROVING KNOWLEDGE AND CLASSIFICATION OF GLAUCOMA 133 8.2 THEORY AND
METHODS 134
8.2.1 DATASETS 134
8.2.2 BAYESIAN NETWORKS 135
8.2.3 COMBINING NETWORKS 140
8.3 ALGORITHMS 141
8.3.1 LEARNING THE STRUCTURE 141
8.3.2 COMBINING TWO NETWORKS 142
8.3.3 OPTIMIZED COMBINATION 143
8.4 RESULTS AND PERFORMANCE EVALUATION 143
8.4.1 BASE CLASSIFIERS 143
8.4.2 ENSEMBLES OF CLASSIFIERS 144
REFERENCES 148
9 A NOVEL ENSEMBLE TECHNIQUE FOR PROTEIN SUBCELLULAR LOCATION PREDICTION
151
ALESSANDRO ROZZA, GABRIELE LOMBARDI, MATTEO RE, ELENA CASIRAGHI, GIORGIO
VALENTINI, PAOLA CAMPADELH 9.1 INTRODUCTION 151
9.2 RELATED WORKS 153
9.3 CLASSIFIERS BASED ON EFFICIENT FISHER SUBSPACE ESTIMATION 156 9.3.1
A KERNEL VERSION OF TIPCAC 157
9.4 DDAG K-TIPCAC 158
9.4.1 DECISION DAGS (DDAGS) 158
9.4.2 DECISION DAG K-TIPCAC 158
9.5 EXPERIMENTAL SETTING 159
9.5.1 METHODS 159
9.5.2 DATASET 160
9.5.3 PERFORMANCE EVALUATION 161
9.6 RESULTS 161
9.6.1 DDAG K-TIPCAC EMPLOYING THE STANDARD MULTICLASS ESTIMATION OF FS
163
9.6.2 DDAG K-TIPCAC WITHOUT PROJECTION ON MULTICLASS FS. 164 9.7
CONCLUSION 165
REFERENCES 166
IMAGE 5
CONTENTS XV
10 TRADING-OFF DIVERSITY AND ACCURACY FOR OPTIMAL ENSEMBLE TREE
SELECTION IN RANDOM FORESTS 169
HAYTHAM ELGHAZEL, ALEX AUSSEM, FLORENCE PERRAUD 10.1 INTRODUCTION 169
10.2 BACKGROUND OF ENSEMBLE SELECTION 171
10.3 CONTRIBUTION 172
10.4 EMPIRICAL RESULTS 174
10.4.1 EXPERIMENTS ON BENCHMARK DATA SETS 174
10.4.2 EXPERIMENTS ON REAL DATA SETS 175
10.5 CONCLUSION 177
REFERENCES 178
11 RANDOM ORACLES FOR REGRESSION ENSEMBLES 181
CARLOS PARDO, JUAN J. RODRIGUEZ, JOSE F. DIEZ-PASTOR, CESAR
GARCIA-OSORIO 11.1 INTRODUCTION 181
11.2 RANDOM ORACLES 183
11.3 EXPERIMENTS 183
.4 RESULTS 185
.5 DIVERSITY-ERROR DIAGRAMS 191
.6 CONCLUSION 194
REFERENCES 198
12 EMBEDDING RANDOM PROJECTIONS IN REGULARIZED GRADIENT BOOSTING
MACHINES 201
PIERLUIGI CASALE, ORIOL PUJOL, PETIA RADEVA 12.1 INTRODUCTION 201
12.2 RELATED WORKS ON RPS 202
12.3 METHODS 203
12.3.1 GRADIENT BOOSTING MACHINES 203
12.3.2 RANDOM PROJECTIONS 204
12.3.3 RANDOM PROJECTIONS IN BOOSTING MACHINE 205 12.4 EXPERIMENTS AND
RESULTS 206
12.4.1 TEST PATTERNS 207
12.4.2 UCI DATASETS 209
12.4.3 THE EFFECT OF REGULARIZATION IN RPBOOST 211
12.4.4 DISCUSSION 214
12.5 CONCLUSION 215
REFERENCES 216
13 AN IMPROVED MIXTURE OF EXPERTS MODEL: DIVIDE AND CONQUER USING RANDOM
PROTOTYPES 217
GIULIANO ARMANO, NIMA HATAMI 13.1 INTRODUCTION 217
13.2 STANDARD MIXTURE OF EXPERTS MODELS 220
13.2.1 STANDARD ME MODEL 220
IMAGE 6
XVI CONTENTS
13.2.2 STANDARD HME MODEL 221
13.3 MIXTURE OF RANDOM PROTOTYPE-BASED EXPERTS (MRPE) AND HIERARCHICAL
MRPE 222
13.3.1 MIXTURE OF RANDOM PROTOTYPE-BASED LOCAL EXPERTS 222 13.3.2
HIERARCHICAL MRPE MODEL 225
13.4 EXPERIMENTAL RESULTS AND DISCUSSION 227
13.5 CONCLUSION 230
REFERENCES 230
14 T H R EE D A TA P A R T I T I O N I NG S T R A T E G I ES FOR B U I L
D I NG L O C AL C L A S S I F I E R S . . . 2 33
INDRE ZLIOBAITE 14.1 INTRODUCTION 233
14.2 THREE ALTERNATIVES FOR BUILDING LOCAL CLASSIFIERS 234 14.2.1
INSTANCE BASED PARTITIONING 235
14.2.2 INSTANCE BASED PARTITIONING WITH LABEL INFORMATION 236 14.2.3
PARTITIONING USING ONE FEATURE 236
14.3 ANALYSIS WITH THE MODELING DATASET 238
14.3.1 TESTING SCENARIO 239
14.3.2 RESULTS 242
14.4 EXPERIMENTS WITH REAL DATA 242
14.4.1 DATASETS 242
14.4.2 IMPLEMENTATION DETAILS 243
14.4.3 EXPERIMENTAL GOALS 243
14.4.4 RESULTS 244
14.5 CONCLUSION 249
REFERENCES 250
INDEX 251 |
| any_adam_object | 1 |
| author2 | Okun, Oleg |
| author2_role | edt |
| author2_variant | o o oo |
| author_facet | Okun, Oleg |
| building | Verbundindex |
| bvnumber | BV039670176 |
| classification_rvk | ST 300 |
| ctrlnum | (OCoLC)762115779 (DE-599)DNB1013327136 |
| dewey-full | 006.31 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.31 |
| dewey-search | 006.31 |
| dewey-sort | 16.31 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Book |
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| genre | (DE-588)1071861417 Konferenzschrift 2010 Barcelona gnd-content |
| genre_facet | Konferenzschrift 2010 Barcelona |
| id | DE-604.BV039670176 |
| illustrated | Illustrated |
| indexdate | 2024-07-21T00:14:24Z |
| institution | BVB |
| isbn | 9783642229091 3642229093 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-024519347 |
| oclc_num | 762115779 |
| open_access_boolean | |
| owner | DE-11 DE-473 DE-BY-UBG |
| owner_facet | DE-11 DE-473 DE-BY-UBG |
| physical | XX, 252 S. Ill., graph. Darst. 24 cm |
| publishDate | 2011 |
| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | Springer |
| record_format | marc |
| series | Studies in computational intelligence |
| series2 | Studies in computational intelligence |
| spelling | Ensembles in machine learning applications Oleg Okun ... (ed.) Berlin ; Heidelberg Springer 2011 XX, 252 S. Ill., graph. Darst. 24 cm txt rdacontent n rdamedia nc rdacarrier Studies in computational intelligence 373 Literaturangaben Klassifikator Informatik (DE-588)4288547-4 gnd rswk-swf Fehlerkorrekturcode (DE-588)4124917-3 gnd rswk-swf Überwachtes Lernen (DE-588)4580264-6 gnd rswk-swf Merkmalsextraktion (DE-588)4314440-8 gnd rswk-swf Soft Computing (DE-588)4455833-8 gnd rswk-swf Bootstrap-Aggregation (DE-588)7549237-4 gnd rswk-swf Bayes-Netz (DE-588)4567228-3 gnd rswk-swf Unüberwachtes Lernen (DE-588)4580265-8 gnd rswk-swf (DE-588)1071861417 Konferenzschrift 2010 Barcelona gnd-content Überwachtes Lernen (DE-588)4580264-6 s Unüberwachtes Lernen (DE-588)4580265-8 s Bayes-Netz (DE-588)4567228-3 s Bootstrap-Aggregation (DE-588)7549237-4 s Fehlerkorrekturcode (DE-588)4124917-3 s Merkmalsextraktion (DE-588)4314440-8 s Klassifikator Informatik (DE-588)4288547-4 s Soft Computing (DE-588)4455833-8 s DE-604 Okun, Oleg edt Erscheint auch als Online-Ausgabe Ensembles in Machine Learning Applications Studies in computational intelligence 373 (DE-604)BV020822171 373 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=3850542&prov=M&dok_var=1&dok_ext=htm Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024519347&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Ensembles in machine learning applications Studies in computational intelligence Klassifikator Informatik (DE-588)4288547-4 gnd Fehlerkorrekturcode (DE-588)4124917-3 gnd Überwachtes Lernen (DE-588)4580264-6 gnd Merkmalsextraktion (DE-588)4314440-8 gnd Soft Computing (DE-588)4455833-8 gnd Bootstrap-Aggregation (DE-588)7549237-4 gnd Bayes-Netz (DE-588)4567228-3 gnd Unüberwachtes Lernen (DE-588)4580265-8 gnd |
| subject_GND | (DE-588)4288547-4 (DE-588)4124917-3 (DE-588)4580264-6 (DE-588)4314440-8 (DE-588)4455833-8 (DE-588)7549237-4 (DE-588)4567228-3 (DE-588)4580265-8 (DE-588)1071861417 |
| title | Ensembles in machine learning applications |
| title_auth | Ensembles in machine learning applications |
| title_exact_search | Ensembles in machine learning applications |
| title_full | Ensembles in machine learning applications Oleg Okun ... (ed.) |
| title_fullStr | Ensembles in machine learning applications Oleg Okun ... (ed.) |
| title_full_unstemmed | Ensembles in machine learning applications Oleg Okun ... (ed.) |
| title_short | Ensembles in machine learning applications |
| title_sort | ensembles in machine learning applications |
| topic | Klassifikator Informatik (DE-588)4288547-4 gnd Fehlerkorrekturcode (DE-588)4124917-3 gnd Überwachtes Lernen (DE-588)4580264-6 gnd Merkmalsextraktion (DE-588)4314440-8 gnd Soft Computing (DE-588)4455833-8 gnd Bootstrap-Aggregation (DE-588)7549237-4 gnd Bayes-Netz (DE-588)4567228-3 gnd Unüberwachtes Lernen (DE-588)4580265-8 gnd |
| topic_facet | Klassifikator Informatik Fehlerkorrekturcode Überwachtes Lernen Merkmalsextraktion Soft Computing Bootstrap-Aggregation Bayes-Netz Unüberwachtes Lernen Konferenzschrift 2010 Barcelona |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=3850542&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=024519347&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV020822171 |
| work_keys_str_mv | AT okunoleg ensemblesinmachinelearningapplications |