Machine Learning: Modeling Data Locally and Globally

✍ Scribed by Dr. Kaizhu Huang, Dr. Haiqin Yang, Prof. Irwin King, Dr. Michael Lyu (auth.)

Publisher: Springer Berlin Heidelberg
Year: 2008
Tongue: English
Leaves: 172
Series: Advanced Topics in Science and Technology in China
Edition: Jointly published with Zhejiang University Press2008
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

Machine Learning - Modeling Data Locally and Globally presents a novel and unified theory that tries to seamlessly integrate different algorithms. Specifically, the book distinguishes the inner nature of machine learning algorithms as either "local learning"or "global learning."This theory not only connects previous machine learning methods, or serves as roadmap in various models, but – more importantly – it also motivates a theory that can learn from data both locally and globally. This would help the researchers gain a deeper insight and comprehensive understanding of the techniques in this field. The book reviews current topics,new theories and applications.

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.

✦ Table of Contents

Front Matter....Pages I-X
Introduction....Pages 1-11
Global Learning vs. Local Learning....Pages 13-27
A General Global Learning Model: MEMPM....Pages 29-68
Learning Locally and Globally: Maxi-Min Margin Machine....Pages 69-95
Extension I: BMPM for Imbalanced Learning....Pages 96-117
Extension II: A Regression Model from M 4 ....Pages 119-132
Extension III: Variational Margin Settings within Local Data in Support Vector Regression....Pages 133-159
Conclusion and Future Work....Pages 161-165
Back Matter....Pages 167-169

✦ Subjects

Pattern Recognition; Information Storage and Retrieval; Data Mining and Knowledge Discovery

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