遷移學習：理論與實踐

Preface
Chapter 1 Introduction
1.1 Background and Motivation
1.2 COntributiong
1.2.1 Extended MDLP for Transfer Learning
1.2.2 Compact Coding for Hyperplane Classifiers in Transfer Learning
1.2.3 Transfer Active Learning
1.2.4 Gaussian Process for Transfer Learning
1.3 Book OverviewChapter 2 Literature Review and Preliminaries for MDLP
2.1 Transfer Learning
2.2 Active Learning and Transfer Active Learning
2.3 Preljminaries for MD[.PChapter 3 Extended MDL Principle for Feature-based Transfer
Learning
3.1 IntroductiOn
3.2 Problem Statement
3.3 Preliminaries for Encoding
3.3.1 Theoretical Foundation of the EMDLP
3.3.2 Adaptation of the EMDLP to Our Problem
3.4 Supervised Inductive Transfer Learning Algorithm
3.4.1 EMDLP with Incremental Search
3.4.2 EMDLP with Hill Climbing
3.5 Experiments
3.5.1 Experimental Settings
3.5.2 Experimental Results on Synthetic Data Sets
3.5.3 Experimental Results on Real Data Sets
3.6 SummaryChapter 4 Compact Coding for Hyperplane Classifiers in a
Heterogeneous Environment
4.1 Introduction
4.2 Problem Setting
4.3 Compact Coding for Hyperplane Classifiers in
Heterogeneous Environment
4.3.1 Macro Level：Arrange Related Tasks
4.3.2 Micro Level Evaluation
4.3.3 The Transfer Learning Algorithm
4.4 Experiments
4.4.1 Experimental Setting
4.4.2 Experimental Results
4.5 SummaryChapter 5 Adaptive Transfer Learning with Query by
Committee
5.1 IntroductiOn
5.2 Problem Setting and Preliminaries
5.3 Probabilistic Framework for ALTL
5.4 The ALTL Algorithm and Analysis
5.4.1 The Procedure of ALTL
5.4.2 Termination Condition and Analysis
5.5 Experiments
5.5.1 Experimental Setting
5.5.2 Results on Synthetic Data Sets
5.5.3 Results on Real Data Sets
5.6 SummaryChapter 6 Gaussian Process for Transfer Learning through
Minimum Encoding
6.1 IntrOduction
6.2 Gaussian Process for Classification
6.3 The GPTL Algorithm
6.3.1 Arrange Related Tasks
6.3.2 The Instance Level Similarities
6.4 Experiments
6.5 SummaryChapter 7 Concluding Comments
Appendix A Target Concepts in Chapter 3
Bibliography