遷移學(xué)習(xí)：理論與實(shí)踐

定　價：￥29.00

作　者：	邵浩著
出版社：	上海交通大學(xué)出版社
叢編項：
標(biāo)　簽：	暫缺

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ISBN：	9787313106568	出版時間：	2013-12-01	包裝：	平裝
開本：	16開	頁數(shù)：	121	字?jǐn)?shù)：

內(nèi)容簡介

　　《遷移學(xué)習(xí)：理論與實(shí)踐》著眼于管理實(shí)際中的資源再利用，對數(shù)據(jù)挖掘領(lǐng)域最前沿的遷移學(xué)習(xí)進(jìn)行了詳細(xì)闡述，并著重介紹了應(yīng)用最為廣泛的分類學(xué)習(xí)，將最前沿的研究進(jìn)行了歸納總結(jié)，并通過實(shí)際算法分析，將領(lǐng)域內(nèi)的最新進(jìn)展提供給讀者，使讀者能夠使用遷移學(xué)習(xí)的工具構(gòu)建模型并應(yīng)用到實(shí)際問題?！哆w移學(xué)習(xí)：理論與實(shí)踐》主要讀者對象為具有管理和計算機(jī)背景并在數(shù)據(jù)挖掘領(lǐng)域有初步研究的學(xué)者。

作者簡介

　　邵浩，上海對外經(jīng)貿(mào)大學(xué)WTO研究教育學(xué)院講師，日本國立九州大學(xué)工學(xué)博士，曾就讀于中國科學(xué)技術(shù)大學(xué)管理學(xué)院碩博連讀課程。研究方向為數(shù)據(jù)挖掘、管理科學(xué)與工程。

圖書目錄

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