TY  - BOOK
AU  - Huang,Kaizhu
AU  - Yang,Haiqin
AU  - King,Irwin
AU  - Lyu,Michael
ED  - SpringerLink (Online service)
TI  - Machine Learning: Modeling Data Locally and Globally 
T2  - Advanced Topics in Science and Technology in China,
SN  - 9783540794523
AV  - Q337.5 
U1  - 006.4 23
PY  - 2008///
CY  - Berlin, Heidelberg
PB  - Springer Berlin Heidelberg
KW  - Computer science
KW  - Data mining
KW  - Information storage and retrieval
KW  - Pattern recognition
KW  - Computer Science
KW  - Pattern Recognition
KW  - Information Storage and Retrieval
KW  - Data Mining and Knowledge Discovery
KW  - Electronic books
KW  - local
N1  - Global Learning vs. Local Learning -- A General Global Learning Model: MEMPM -- Learning Locally and Globally: Maxi-Min Margin Machine -- Extension I: BMPM for Imbalanced Learning -- Extension II: A Regression Model from M4 -- Extension III: Variational Margin Settings within Local Data in Support Vector Regression -- Conclusion and Future Work
N2  - 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
UR  - http://ezproxy.alfaisal.edu/login?url=http://dx.doi.org/10.1007/978-3-540-79452-3
ER  -