Boosting: foundations and algorithms
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, MA [u.a.]
MIT Press
2012
|
| Schriftenreihe: | Adaptive computation and machine learning
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XV, 526 S. Ill., graph. Darst. |
| ISBN: | 9780262017183 9780262526036 |
Internformat
MARC
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| 100 | 1 | |a Schapire, Robert E. |d 1963-1963 |e Verfasser |0 (DE-588)1076833217 |4 aut | |
| 245 | 1 | 0 | |a Boosting |b foundations and algorithms |c Robert E. Schapire ; Yoav Freund |
| 264 | 1 | |a Cambridge, MA [u.a.] |b MIT Press |c 2012 | |
| 300 | |a XV, 526 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Adaptive computation and machine learning | |
| 500 | |a Includes bibliographical references and index | ||
| 650 | 4 | |a Boosting (Algorithms) | |
| 650 | 4 | |a Supervised learning (Machine learning) | |
| 650 | 0 | 7 | |a Boosting |0 (DE-588)4839853-6 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Teilüberwachtes Lernen |0 (DE-588)4782452-9 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 655 | 7 | |0 (DE-588)4123623-3 |a Lehrbuch |2 gnd-content | |
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| 999 | |a oai:aleph.bib-bvb.de:BVB01-025397491 | ||
Datensatz im Suchindex
| _version_ | 1804149645439401984 |
|---|---|
| adam_text | Titel: Boosting
Autor: Schapire, Robert E
Jahr: 2012
Contents
Series Foreword xi
Preface xiii
Introduction and Overview 1
1.1 Classification Problems and Machine Learning 2
1.2 Boosting 4
1.3 Resistance to Overfitting and the Margins Theory 14
1.4 Foundations and Algorithms 17
Summary 19
Bibliographic Notes 19
Exercises 20
CORE ANALYSIS 21
Foundations of Machine Learning 23
2.1 A Direct Approach to Machine Learning 24
2.2 General Methods of Analysis 30
2.3 A Foundation for the Study of Boosting Algorithms 43
Summary 49
Bibliographic Notes 49
Exercises 50
Using AdaBoost to Minimize Training Error 53
3.1 A Bound on AdaBoost s Training Error 54
3.2 A Sufficient Condition for Weak Learnability 56
3.3 Relation to Chernoff Bounds 60
3.4 Using and Designing Base Learning Algorithms 62
Summary 70
Bibliographic Notes 71
Exercises 71
Contents
4 Direct Bounds on the Generalization Error 75
4.1 Using VC Theory to Bound the Generalization Error 75
4.2 Compression-Based Bounds 83
4.3 The Equivalence of Strong and Weak Learnability 86
Summary 88
Bibliographic Notes 89
Exercises 89
5 The Margins Explanation for Boosting s Effectiveness 93
5.1 Margin as a Measure of Confidence 94
5.2 A Margins-Based Analysis of the Generalization Error 97
5.3 Analysis Based on Rademacher Complexity 106
5.4 The Effect of Boosting on Margin Distributions 111
5.5 Bias, Variance, and Stability 117
5.6 Relation to Support-Vector Machines 122
5.7 Practical Applications of Margins 128
Summary 132
Bibliographic Notes 132
Exercises 134
II FUNDAMENTAL PERSPECTIVES 139
6 Game Theory, Online Learning, and Boosting 141
6.1 Game Theory 142
6.2 Learning in Repeated Game Playing 145
6.3 Online Prediction 153
6.4 Boosting 157
6.5 Application to a Mind-Reading Game 163
Summary 169
Bibliographic Notes 169
Exercises 170
7 Loss Minimization and Generalizations of Boosting 175
7.1 AdaBoost s Loss Function 177
7.2 Coordinate Descent 179
7.3 Loss Minimization Cannot Explain Generalization 184
7.4 Functional Gradient Descent 188
7.5 Logistic Regression and Conditional Probabilities 194
7.6 Regularization 202
7.7 Applications to Data-Limited Learning 211
Summary 219
Bibliographic Notes 219
Exercises 220
Contents
8 Boosting, Convex Optimization, and Information Geometry 227
8.1 Iterative Projection Algorithms 228
8.2 Proving the Convergence of AdaBoost 243
8.3 Unification with Logistic Regression 252
8.4 Application to Species Distribution Modeling 255
Summary 260
Bibliographic Notes 262
Exercises 263
III ALGORITHMIC EXTENSIONS 269
9 Using Confidence-Rated Weak Predictions 271
9.1 The Framework 273
9.2 General Methods for Algorithm Design 275
9.3 Learning Rule-Sets 287
9.4 Alternating Decision Trees 290
Summary 296
Bibliographic Notes 297
Exercises 297
10 Multiclass Classification Problems 303
10.1 A Direct Extension to the Multiclass Case 305
10.2 The One-against-All Reduction and Multi-label Classification 310
10.3 Application to Semantic Classification 316
10.4 General Reductions Using Output Codes 320
Summary 333
Bibliographic Notes 333
Exercises 334
11 Learning to Rank 341
11.1 A Formal Framework for Ranking Problems 342
11.2 A Boosting Algorithm for the Ranking Task 345
11.3 Methods for Improving Efficiency 351
11.4 Multiclass, Multi-label Classification 361
11.5 Applications 364
Summary 367
Bibliographic Notes 369
Exercises 369
Contents
IV ADVANCED THEORY 375
12 Attaining the Best Possible Accuracy 377
12.1 Optimality in Classification and Risk Minimization 378
12.2 Approaching the Optimal Risk 382
12.3 How Minimizing Risk Can Lead to Poor Accuracy 398
Summary 406
Bibliographic Notes 406
Exercises 407
13 Optimally Efficient Boosting 415
13.1 The Boost-by-Majority Algorithm 416
13.2 Optimal Generalization Error 432
13.3 Relation to AdaBoost 448
Summary 453
Bibliographic Notes 453
Exercises 453
14 Boosting in Continuous Time 459
14.1 Adapti veness in the Limit of Continuous Time 460
14.2 BrownBoost 468
14.3 AdaBoost as a Special Case of BrownBoost 476
14.4 Experiments with Noisy Data 483
Summary 485
Bibliographic Notes 486
Exercises 486
Appendix: Some Notation, Definitions, and Mathematical Background 491
A.l General Notation 491
A.2 Norms 492
A.3 Maxima, Minima, Suprema, and Infima 493
A.4 Limits 493
A.5 Continuity, Closed Sets, and Compactness 494
A.6 Derivatives, Gradients, and Taylor s Theorem 495
A.7 Convexity 496
A.8 The Method of Lagrange Multipliers 497
A.9 Some Distributions and the Central Limit Theorem 498
Bibliography 501
Index of Algorithms, Figures, and Tables 511
Subject and Author Index 513
|
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| isbn | 9780262017183 9780262526036 |
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| series2 | Adaptive computation and machine learning |
| spelling | Schapire, Robert E. 1963-1963 Verfasser (DE-588)1076833217 aut Boosting foundations and algorithms Robert E. Schapire ; Yoav Freund Cambridge, MA [u.a.] MIT Press 2012 XV, 526 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Adaptive computation and machine learning Includes bibliographical references and index Boosting (Algorithms) Supervised learning (Machine learning) Boosting (DE-588)4839853-6 gnd rswk-swf Teilüberwachtes Lernen (DE-588)4782452-9 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Boosting (DE-588)4839853-6 s Maschinelles Lernen (DE-588)4193754-5 s Teilüberwachtes Lernen (DE-588)4782452-9 s DE-604 Freund, Yoav 1961- Verfasser (DE-588)1076833527 aut Erscheint auch als Online-Ausgabe 978-0-262-30118-3 0-262-30118-0 (DE-604)BV042509038 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025397491&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Schapire, Robert E. 1963-1963 Freund, Yoav 1961- Boosting foundations and algorithms Boosting (Algorithms) Supervised learning (Machine learning) Boosting (DE-588)4839853-6 gnd Teilüberwachtes Lernen (DE-588)4782452-9 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4839853-6 (DE-588)4782452-9 (DE-588)4193754-5 (DE-588)4123623-3 |
| title | Boosting foundations and algorithms |
| title_auth | Boosting foundations and algorithms |
| title_exact_search | Boosting foundations and algorithms |
| title_full | Boosting foundations and algorithms Robert E. Schapire ; Yoav Freund |
| title_fullStr | Boosting foundations and algorithms Robert E. Schapire ; Yoav Freund |
| title_full_unstemmed | Boosting foundations and algorithms Robert E. Schapire ; Yoav Freund |
| title_short | Boosting |
| title_sort | boosting foundations and algorithms |
| title_sub | foundations and algorithms |
| topic | Boosting (Algorithms) Supervised learning (Machine learning) Boosting (DE-588)4839853-6 gnd Teilüberwachtes Lernen (DE-588)4782452-9 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Boosting (Algorithms) Supervised learning (Machine learning) Boosting Teilüberwachtes Lernen Maschinelles Lernen Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025397491&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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