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   源码介绍

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Contents
Preface page ix
PART I FOUNDATIONS OF TRANSFER LEARNING 1
1 Introduction 3
1.1 AI, Machine Learning and Transfer Learning 3
1.2 Transfer Learning: A Definition 7
1.3 Relationship to Existing Machine Learning Paradigms 11
1.4 Fundamental Research Issues in Transfer Learning 13
1.5 Applications of Transfer Learning 14
1.6 Historical Notes 17
1.7 About This Book 18
2 Instance-Based Transfer Learning 23
2.1 Introduction 23
2.2 Instance-Based Noninductive Transfer Learning 25
2.3 Instance-Based Inductive Transfer Learning 28
3 Feature-Based Transfer Learning 34
3.1 Introduction 34
3.2 Minimizing the Domain Discrepancy 35
3.3 Learning Universal Features 41
3.4 Feature Augmentation 43
4 Model-Based Transfer Learning 45
4.1 Introduction 45
4.2 Transfer through Shared Model Components 47
4.3 Transfer through Regularization 50
5 Relation-Based Transfer Learning 58
5.1 Introduction 58
5.2 Markov Logic Networks 61
5.3 Relation-Based Transfer Learning Based on MLNs 61
vi Contents
6 Heterogeneous Transfer Learning 68
6.1 Introduction 68
6.2 The Heterogeneous Transfer Learning Problem 70
6.3 Methodologies 71
6.4 Applications 90
7 Adversarial Transfer Learning 93
7.1 Introduction 93
7.2 Generative Adversarial Networks 94
7.3 Transfer Learning with Adversarial Models 97
7.4 Discussion 104
8 Transfer Learning in Reinforcement Learning 105
8.1 Introduction 105
8.2 Background 107
8.3 Inter-task Transfer Learning 113
8.4 Inter-domain Transfer Learning 122
9 Multi-task Learning 126
9.1 Introduction 126
9.2 The Definition 128
9.3 Multi-task Supervised Learning 128
9.4 Multi-task Unsupervised Learning 137
9.5 Multi-task Semi-supervised Learning 138
9.6 Multi-task Active Learning 138
9.7 Multi-task Reinforcement Learning 139
9.8 Multi-task Online Learning 139
9.9 Multi-task Multi-view Learning 140
9.10 Parallel and Distributed Multi-task Learning 140
10 Transfer Learning Theory 141
10.1 Introduction 141
10.2 Generalization Bounds for Multi-task Learning 142
10.3 Generalization Bounds for Supervised Transfer Learning 145
10.4 Generalization Bounds for Unsupervised Transfer Learning 148
11 Transitive Transfer Learning 151
11.1 Introduction 151
11.2 TTL over Mixed Graphs 153
11.3 TTL with Hidden Feature Representations 158
11.4 TTL with Deep Neural Networks 162
12 AutoTL: Learning to Transfer Automatically 168
12.1 Introduction 168
12.2 The L2T Framework 169
12.3 Parameterizing What to Transfer 170
12.4 Learning from Experiences 171
Contents vii
12.5 Inferring What to Transfer 174
12.6 Connections to Other Learning Paradigms 174
13 Few-Shot Learning 177
13.1 Introduction 177
13.2 Zero-Shot Learning 178
13.3 One-Shot Learning 184
13.4 Bayesian Program Learning 187
13.5 Poor Resource Learning 190
13.6 Domain Generalization 193
14 Lifelong Machine Learning 196
14.1 Introduction 196
14.2 Lifelong Machine Learning: A Definition 197
14.3 Lifelong Machine Learning through Invariant Knowledge 198
14.4 Lifelong Machine Learning in Sentiment Classification 199
14.5 Shared Model Components as Multi-task Learning 203
14.6 Never-Ending Language Learning 204
PART II APPLICATIONS OF TRANSFER LEARNING 209
15 Privacy-Preserving Transfer Learning 211
15.1 Introduction 211
15.2 Differential Privacy 212
15.3 Privacy-Preserving Transfer Learning 215
16 Transfer Learning in Computer Vision 221
16.1 Introduction 221
16.2 Overview 222
16.3 Transfer Learning for Medical Image Analysis 229
17 Transfer Learning in Natural Language Processing 234
17.1 Introduction 234
17.2 Transfer Learning in NLP 234
17.3 Transfer Learning in Sentiment Analysis 241
18 Transfer Learning in Dialogue Systems 257
18.1 Introduction 257
18.2 Problem Formulation 259
18.3 Transfer Learning in Spoken Language Understanding 259
18.4 Transfer Learning in Dialogue State Tracker 262
18.5 Transfer Learning in DPL 263
18.6 Transfer Learning in Natural Language Generation 268
18.7 Transfer Learning in End-to-End Dialogue Systems 269
viii Contents
19 Transfer Learning in Recommender Systems 279
19.1 Introduction 279
19.2 What to Transfer in Recommendation 280
19.3 News Recommendation 284
19.4 VIP Recommendation in Social Networks 288
20 Transfer Learning in Bioinformatics 293
20.1 Introduction 293
20.2 Machine Learning Problems in Bioinformatics 294
20.3 Biological Sequence Analysis 295
20.4 Gene Expression Analysis and Genetic Analysis 299
20.5 Systems Biology 299
20.6 Biomedical Text and Image Mining 301
20.7 Deep Learning for Bioinformatics 302
21 Transfer Learning in Activity Recognition 307
21.1 Introduction 307
21.2 Transfer Learning for Wireless Localization 307
21.3 Transfer Learning for Activity Recognition 316
22 Transfer Learning in Urban Computing 324
22.1 Introduction 324
22.2 “What to Transfer” in Urban Computing 325
22.3 Key Issues of Transfer Learning in Urban Computing 326
22.4 Chain Store Recommendation 327
22.5 Air-Quality Prediction 330
23 Concluding Remarks 334
References 336
Index 377