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迁移学习 理论与实践2025|PDF|Epub|mobi|kindle电子书版本百度云盘下载

迁移学习 理论与实践
  • 邵浩著 著
  • 出版社： 上海：上海交通大学出版社
  • ISBN：9787313106568
  • 出版时间：2013
  • 标注页数：121页
  • 文件大小：16MB
  • 文件页数：130页
  • 主题词：数据采集－研究

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图书目录

Chapter 1 Introduction1

1.1 Background and Motivation1

1.2 Contributions5

1.2.1 Extended MDLP for Transfer Learning5

1.2.2 Compact Coding for Hyperplane Classifiers in Transfer Learning6

1.2.3 Transfer Active Learning7

1.2.4 Gaussian Process for Transfer Learning8

1.3 Book Overview9

Chapter 2 Literature Review and Preliminaries for MDLP10

2.1 Transfer Learning10

2.2 Active Learning and Transfer Active Learning13

2.3 Preliminaries for MDLP14

Chapter 3 Extended MDL Principle for Feature-based Transfer Learning17

3.1 Introduction17

3.2 Problem Statement20

3.3 Preliminaries for Encoding21

3.3.1 Theoretical Foundation of the EMDLP22

3.3.2 Adaptation of the EMDLP to Our Problem25

3.4 Supervised Inductive Transfer Learning Algorithm30

3.4.1 EMDLP with Incremental Search30

3.4.2 EMDLP with Hill Climbing33

3.5 Experiments36

3.5.1 Experimental Settings36

3.5.2 Experimental Results on Synthetic Data Sets40

3.5.3 Experimental Results on Real Data Sets45

3.6 Summary52

Chapter 4 Compact Coding for Hyperplane Classifiers in a Heterogeneous Environment53

4.1 Introduction53

4.2 Problem Setting55

4.3 Compact Coding for Hyperplane Classifiers in Heterogeneous Environment56

4.3.1 Macro Level:Arrange Related Tasks57

4.3.2 Micro Level Evaluation61

4.3.3 The Transfer Learning Algorithm62

4.4 Experiments63

4.4.1 Experimental Setting63

4.4.2 Experimental Results65

4.5 Summary71

Chapter 5 Adaptive Transfer Learning with Query by Committee72

5.1 Introduction72

5.2 Problem Setting and Preliminaries75

5.3 Probabilistic Framework for ALTL78

5.4 The ALTL Algorithm and Analysis81

5.4.1 The Procedure of ALTL81

5.4.2 Termination Condition and Analysis83

5.5 Experiments85

5.5.1 Experimental Setting85

5.5.2 Results on Synthetic Data Sets85

5.5.3 Results on Real Data Sets89

5.6 Summary93

Chapter 6 Gaussian Process for Transfer Learning through Minimum Encoding94

6.1 Introduction94

6.2 Gaussian Process for Classification96

6.3 The GPTL Algorithm97

6.3.1 Arrange Related Tasks97

6.3.2 The Instance Level Similarities99

6.4 Experiments100

6.5 Summary104

Chapter 7 Concluding Comments106

Appendix A Target Concepts in Chapter 3110

Bibliography113

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