Machine Learning Modeling Data Locally and Globally /

• Κύριοι συγγραφείς: Huang, Kaizhu (Συγγραφέας), Yang, Haiqin (Συγγραφέας), King, Irwin (Συγγραφέας), Lyu, Michael (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.
• Σειρά: Advanced Topics in Science and Technology in China,
• Θέματα: Computer science.; Data mining.; Information storage and retrieval.; Pattern recognition.; Computer Science.; Pattern Recognition.; Information Storage and Retrieval.; Data Mining and Knowledge Discovery.

 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. 

 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.