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Machine Learning Modeling Data Locally and Globally /

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."T...

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Main Authors: Huang, Kai-Zhu. (Author, http://id.loc.gov/vocabulary/relators/aut), Yang, Haiqin. (http://id.loc.gov/vocabulary/relators/aut), King, Irwin. (http://id.loc.gov/vocabulary/relators/aut), Lyu, Michael R. (http://id.loc.gov/vocabulary/relators/aut)
Corporate Author: SpringerLink (Online service)
Language:English
Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2008.
Edition:1st ed. 2008.
Series:Advanced Topics in Science and Technology in China,
Subjects:
Pattern recognition.
Information storage and retrieval.
Data mining.
Pattern Recognition.
Information Storage and Retrieval.
Data Mining and Knowledge Discovery.
Online Access:https://doi.org/10.1007/978-3-540-79452-3
Table of Contents:
  • 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.

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