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Machine Learning Modeling Data Locally and Globally

:	Kai-Zhu Huang, Haiqin Yang, Michael R. Lyu
:	Machine Learning Modeling Data Locally and Globally
:	Springer-Verlag
:	9783540794523
:	Advanced Topics in Science and Technology in China
:	1
:	CHF 114.00
:

:	Anwendungs-Software
:	English

:	169
:	DRM
:	PC/MAC/eReader/Tablet
:	PDF

Kaizhu Huang was a researcher at the Fujitsu Research and Development Center and is currently a research fellow in the Chinese University of Hong Kong. Haiqin Yang leads the image processing group at HiSilicon Technologies. Irwin King and Michael R. Lyu are professors at the Computer Science and Engineering department of the Chinese University of Hong Kong.

	Preface	6
	Contents	7
	1 Introduction	11
	1.1 Learning and Global Modeling	11
	1.2 Learning and Local Modeling	13
	1.3 Hybrid Learning	15
	1.4 Major Contributions	15
	1.5 Scope	18
	References	19
	2 Global Learning vs. Local Learning	23
	2.1 Problem De.nition	25
	2.2 Global Learning	26
	2.4 Hybrid Learning	33
	2.5 Maxi-Min Margin Machine	34
	References	35
	3 A General Global Learning Model: MEMPM	39
	3.1 Marshall and Olkin Theory	40
	3.2 Minimum Error Minimax Probability Decision Hyperplane	41
	3.3 Robust Version	55
	3.4 Kernelization	56
	3.5 Experiments	60
	3.6 How Tight Is the Bound?	66
	3.7 On the Concavity of MEMPM	70
	3.8 Limitations and Future Work	75
	3.9 Summary	76
	References	77
	4 Learning Locally and Globally: Maxi-Min Margin Machine	79
	4.1 Maxi-Min Margin Machine	81
	4.2 Bound on the Error Rate	92
	4.3 Reduction	94
	4.4 Kernelization	95
	4.5 Experiments	98
	4.6 Discussions and Future Work	103
	4.7 Summary	103
	References	104
	5 Extension I: BMPM for Imbalanced Learning	107
	5.1 Introduction to Imbalanced Learning	108
	5.2 Biased Minimax Probability Machine	108
	5.3 Learning from Imbalanced Data by Using BMPM	110
	5.4 Experimental Results	112
	5.5 When the Cost for Each Class Is Known	124
	5.6 Summary	125
	References	125
	6 Extension II: A Regression Model from M4	129
	6.1 A Local Support Vector Regression Model	131
	6.2 Connection with Support Vector Regression	132
	6.3 Link with Maxi-Min Margin Machine	134
	6.4 Optimization Method	134
	6.5 Kernelization	135
	6.6 Additional Interpretation on wTSiw	137
	6.7 Experiments	138
	6.8 Summary	141
	References	141
	7 Extension III: Variational Margin Settings within Local Data in Support Vector Regression	143
	7.1 Support Vector Regression	144
	7.2 Problem in Margin Settings	146
	7.3 General -insensitive Loss Function	146
	7.4 Non-.xed Margin Cases	149
	7.5 Experiments	151
	7.6 Discussions	165
	References	168
	8 Conclusion and Future Work	171
	8.1 Review of the Journey	171
	8.2 Future Work	173
	References	174
	Index	177