Compact Coding in Transfer Learning（簡體書）

邵浩，上海對外經貿大學WTO研究教育學院講師，日本國立九州大學工學博士，曾就讀于中國科學技術大學管理學院碩博連讀課程。研究方向為數據挖掘、管理科學與工程。

《遷移學習--理論與實踐》(作者邵浩)著重介紹了應用最為廣泛的分類學習，將最前沿的研究進行了歸納總結，并通過實際算法分析，將領域內的最新進展提供給讀者，使讀者能夠使用遷移學習的工具構建模型并應用到實際問題，本書適合從事相關研究工作的人員參考閱讀。

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 Overview
Chapter 2 Literature Review and Preliminaries for MDLP
2．1 Transfer Learning
2．2 Active Learning and Transfer Active Learning
2．3 Preljminaries for MD[．P
Chapter 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 Summary
Chapter 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 Summary
Chapter 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 Summary
Chapter 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 Summary
Chapter 7 Concluding Comments
Appendix A Target Concepts in Chapter 3
Bibliography