An elementary introduction to statistical learning theory

221 Pages·2011·1.724 MB·English

by  Sanjeev Kulkarni, Gilbert Harman, Wiley InterScience (Online service)

An Elementary Introduction
to Statistical Learning Theory
WILEY SERIES IN PROBABILITY AND STATISTICS
Established by WALTER A. SHEWHART and SAMUEL S. WILKS
Editors: David J. Balding, Noel A. C. Cressie, Garrett M. Fitzmaurice,
Harvey Goldstein, Iain M. Johnstone, Geert Molenberghs, David W. Scott,
Adrian F. M. Smith, Ruey S. Tsay, Sanford Weisberg
Editors Emeriti: Vic Barnett, J. Stuart Hunter, Joseph B. Kadane, Jozef L. Teugels
A complete list of the titles in this series appears at the end of this volume.
An Elementary Introduction
to Statistical Learning Theory
SANJEEV KULKARNI
Department of Electrical Engineering
School of Engineering and Applied Science
Princeton University
Princeton, New Jersey
GILBERT HARMAN
Department of Philosophy
Princeton University
Princeton, New Jersey
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LibraryofCongressCataloging-in-PublicationData:
Kulkarni,Sanjeev.
Anelementaryintroductiontostatisticallearningtheory/SanjeevKulkarni,GilbertHarman.
p.cm.—(Wileyseriesinprobabilityandstatistics)
Includesindex.
ISBN978-0-470-64183-5 (cloth)
1.Machinelearning–Statisticalmethods.2.Patternrecognitionsystems.I.Harman,Gilbert.II.Title.
Q325.5.K852011
006.3(cid:2)1–dc22
2010045223
PrintedinSingapore
oBookISBN:978-1-118-02347-1
ePDFISBN:978-1-118-02343-3
ePubISBN:978-1-118-02346-4
10987654321
Contents
Preface xiii
1 Introduction: Classification, Learning, Features, and
Applications 1
1.1 Scope, 1
1.2 Why Machine Learning? 2
1.3 Some Applications, 3
1.3.1 Image Recognition, 3
1.3.2 Speech Recognition, 3
1.3.3 Medical Diagnosis, 4
1.3.4 Statistical Arbitrage, 4
1.4 Measurements, Features, and Feature Vectors, 4
1.5 The Need for Probability, 5
1.6 Supervised Learning, 5
1.7 Summary, 6
1.8 Appendix: Induction, 6
1.9 Questions, 7
1.10 References, 8
2 Probability 10
2.1 Probability of Some Basic Events, 10
2.2 Probabilities of Compound Events, 12
2.3 Conditional Probability, 13
2.4 Drawing Without Replacement, 14
2.5 A Classic Birthday Problem, 15
2.6 Random Variables, 15
2.7 Expected Value, 16
v
vi CONTENTS
2.8 Variance, 17
2.9 Summary, 19
2.10 Appendix: Interpretations of Probability, 19
2.11 Questions, 20
2.12 References, 21
3 Probability Densities 23
3.1 An Example in Two Dimensions, 23
3.2 Random Numbers in [0,1], 23
3.3 Density Functions, 24
3.4 Probability Densities in Higher Dimensions, 27
3.5 Joint and Conditional Densities, 27
3.6 Expected Value and Variance, 28
3.7 Laws of Large Numbers, 29
3.8 Summary, 30
3.9 Appendix: Measurability, 30
3.10 Questions, 32
3.11 References, 32
4 The Pattern Recognition Problem 34
4.1 A Simple Example, 34
4.2 Decision Rules, 35
4.3 Success Criterion, 37
4.4 The Best Classifier: Bayes Decision Rule, 37
4.5 Continuous Features and Densities, 38
4.6 Summary, 39
4.7 Appendix: Uncountably Many, 39
4.8 Questions, 40
4.9 References, 41
5 The Optimal Bayes Decision Rule 43
5.1 Bayes Theorem, 43
5.2 Bayes Decision Rule, 44
5.3 Optimality and Some Comments, 45
5.4 An Example, 47
5.5 Bayes Theorem and Decision Rule with Densities, 48
5.6 Summary, 49
5.7 Appendix: Defining Conditional Probability, 50
CONTENTS vii
5.8 Questions, 50
5.9 References, 53
6 Learning from Examples 55
6.1 Lack of Knowledge of Distributions, 55
6.2 Training Data, 56
6.3 Assumptions on the Training Data, 57
6.4 A Brute Force Approach to Learning, 59
6.5 Curse of Dimensionality, Inductive Bias, and No Free Lunch, 60
6.6 Summary, 61
6.7 Appendix: What Sort of Learning? 62
6.8 Questions, 63
6.9 References, 64
7 The Nearest Neighbor Rule 65
7.1 The Nearest Neighbor Rule, 65
7.2 Performance of the Nearest Neighbor Rule, 66
7.3 Intuition and Proof Sketch of Performance, 67
7.4 Using more Neighbors, 69
7.5 Summary, 70
7.6 Appendix: When People use Nearest Neighbor Reasoning, 70
7.6.1 Who Is a Bachelor? 70
7.6.2 Legal Reasoning, 71
7.6.3 Moral Reasoning, 71
7.7 Questions, 72
7.8 References, 73
8 Kernel Rules 74
8.1 Motivation, 74
8.2 A Variation on Nearest Neighbor Rules, 75
8.3 Kernel Rules, 76
8.4 Universal Consistency of Kernel Rules, 79
8.5 Potential Functions, 80
8.6 More General Kernels, 81
8.7 Summary, 82
8.8 Appendix: Kernels, Similarity, and Features, 82
8.9 Questions, 83
8.10 References, 84
viii CONTENTS
9 Neural Networks: Perceptrons 86
9.1 Multilayer Feedforward Networks, 86
9.2 Neural Networks for Learning and Classification, 87
9.3 Perceptrons, 89
9.3.1 Threshold, 90
9.4 Learning Rule for Perceptrons, 90
9.5 Representational Capabilities of Perceptrons, 92
9.6 Summary, 94
9.7 Appendix: Models of Mind, 95
9.8 Questions, 96
9.9 References, 97
10 Multilayer Networks 99
10.1 Representation Capabilities of Multilayer
Networks, 99
10.2 Learning and Sigmoidal Outputs, 101
10.3 Training Error and Weight Space, 104
10.4 Error Minimization by Gradient Descent, 105
10.5 Backpropagation, 106
10.6 Derivation of Backpropagation Equations, 109
10.6.1 Derivation for a Single Unit, 110
10.6.2 Derivation for a Network, 111
10.7 Summary, 113
10.8 Appendix: Gradient Descent and Reasoning toward Reflective
Equilibrium, 113
10.9 Questions, 114
10.10 References, 115
11 PAC Learning 116
11.1 Class of Decision Rules, 117
11.2 Best Rule from a Class, 118
11.3 Probably Approximately Correct Criterion, 119
11.4 PAC Learning, 120
11.5 Summary, 122
11.6 Appendix: Identifying Indiscernibles, 122
11.7 Questions, 123
11.8 References, 123
CONTENTS ix
12 VC Dimension 125
12.1 Approximation and Estimation Errors, 125
12.2 Shattering, 126
12.3 VC Dimension, 127
12.4 Learning Result, 128
12.5 Some Examples, 129
12.6 Application to Neural Nets, 132
12.7 Summary, 133
12.8 Appendix: VC Dimension and Popper Dimension, 133
12.9 Questions, 134
12.10 References, 135
13 Infinite VC Dimension 137
13.1 A Hierarchy of Classes and Modified PAC Criterion, 138
13.2 Misfit Versus Complexity Trade-Off, 138
13.3 Learning Results, 139
13.4 Inductive Bias and Simplicity, 140
13.5 Summary, 141
13.6 Appendix: Uniform Convergence and Universal Consistency, 141
13.7 Questions, 142
13.8 References, 143
14 The Function Estimation Problem 144
14.1 Estimation, 144
14.2 Success Criterion, 145
14.3 Best Estimator: Regression Function, 146
14.4 Learning in Function Estimation, 146
14.5 Summary, 147
14.6 Appendix: Regression Toward the Mean, 147
14.7 Questions, 148
14.8 References, 149
15 Learning Function Estimation 150
15.1 Review of the Function Estimation/Regression Problem, 150
15.2 Nearest Neighbor Rules, 151
15.3 Kernel Methods, 151
15.4 Neural Network Learning, 152
15.5 Estimation with a Fixed Class of Functions, 153
x CONTENTS
15.6 Shattering, Pseudo-Dimension, and Learning, 154
15.7 Conclusion, 156
15.8 Appendix: Accuracy, Precision, Bias, and Variance in
Estimation, 156
15.9 Questions, 157
15.10 References, 158
16 Simplicity 160
16.1 Simplicity in Science, 160
16.1.1 Explicit Appeals to Simplicity, 160
16.1.2 Is the World Simple? 161
16.1.3 Mistaken Appeals to Simplicity, 161
16.1.4 Implicit Appeals to Simplicity, 161
16.2 Ordering Hypotheses, 162
16.2.1 Two Kinds of Simplicity Orderings, 162
16.3 Two Examples, 163
16.3.1 Curve Fitting, 163
16.3.2 Enumerative Induction, 164
16.4 Simplicity as Simplicity of Representation, 165
16.4.1 Fix on a Particular System of Representation? 166
16.4.2 Are Fewer Parameters Simpler? 167
16.5 Pragmatic Theory of Simplicity, 167
16.6 Simplicity and Global Indeterminacy, 168
16.7 Summary, 169
16.8 Appendix: Basic Science and Statistical Learning Theory, 169
16.9 Questions, 170
16.10 References, 170
17 Support Vector Machines 172
17.1 Mapping the Feature Vectors, 173
17.2 Maximizing the Margin, 175
17.3 Optimization and Support Vectors, 177
17.4 Implementation and Connection to Kernel Methods, 179
17.5 Details of the Optimization Problem, 180
17.5.1 Rewriting Separation Conditions, 180
17.5.2 Equation for Margin, 181
17.5.3 Slack Variables for Nonseparable Examples, 181
17.5.4 Reformulation and Solution of Optimization, 182
17.6 Summary, 183