- 作 者：邵浩著
- 出 版 社：上海：上海交通大学出版社
- 出版年份：2013
- ISBN：9787313106568
- 标注页数：121 页
- PDF页数：130 页
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Chapter 1 Introduction 1
1.1 Background and Motivation 1
1.2 Contributions 5
1.2.1 Extended MDLP for Transfer Learning 5
1.2.2 Compact Coding for Hyperplane Classifiers in Transfer Learning 6
1.2.3 Transfer Active Learning 7
1.2.4 Gaussian Process for Transfer Learning 8
1.3 Book Overview 9
Chapter 2 Literature Review and Preliminaries for MDLP 10
2.1 Transfer Learning 10
2.2 Active Learning and Transfer Active Learning 13
2.3 Preliminaries for MDLP 14
Chapter 3 Extended MDL Principle for Feature-based Transfer Learning 17
3.1 Introduction 17
3.2 Problem Statement 20
3.3 Preliminaries for Encoding 21
3.3.1 Theoretical Foundation of the EMDLP 22
3.3.2 Adaptation of the EMDLP to Our Problem 25
3.4 Supervised Inductive Transfer Learning Algorithm 30
3.4.1 EMDLP with Incremental Search 30
3.4.2 EMDLP with Hill Climbing 33
3.5 Experiments 36
3.5.1 Experimental Settings 36
3.5.2 Experimental Results on Synthetic Data Sets 40
3.5.3 Experimental Results on Real Data Sets 45
3.6 Summary 52
Chapter 4 Compact Coding for Hyperplane Classifiers in a Heterogeneous Environment 53
4.1 Introduction 53
4.2 Problem Setting 55
4.3 Compact Coding for Hyperplane Classifiers in Heterogeneous Environment 56
4.3.1 Macro Level:Arrange Related Tasks 57
4.3.2 Micro Level Evaluation 61
4.3.3 The Transfer Learning Algorithm 62
4.4 Experiments 63
4.4.1 Experimental Setting 63
4.4.2 Experimental Results 65
4.5 Summary 71
Chapter 5 Adaptive Transfer Learning with Query by Committee 72
5.1 Introduction 72
5.2 Problem Setting and Preliminaries 75
5.3 Probabilistic Framework for ALTL 78
5.4 The ALTL Algorithm and Analysis 81
5.4.1 The Procedure of ALTL 81
5.4.2 Termination Condition and Analysis 83
5.5 Experiments 85
5.5.1 Experimental Setting 85
5.5.2 Results on Synthetic Data Sets 85
5.5.3 Results on Real Data Sets 89
5.6 Summary 93
Chapter 6 Gaussian Process for Transfer Learning through Minimum Encoding 94
6.1 Introduction 94
6.2 Gaussian Process for Classification 96
6.3 The GPTL Algorithm 97
6.3.1 Arrange Related Tasks 97
6.3.2 The Instance Level Similarities 99
6.4 Experiments 100
6.5 Summary 104
Chapter 7 Concluding Comments 106
Appendix A Target Concepts in Chapter 3 110
Bibliography 113