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Learning Locally and Globally: Maxi-Min Margin Machine

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Machine Learning

Part of the book series: Advanced Topics in Science and Technology in China ((ATSTC))

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Abstract

The proposed MEMPM model obtains the decision hyperplane by using only global information, e.g. the mean and covariance matrices. However, although these moments can be more reliably obtained than estimating the distribution, they may still be inaccurate in many cases, e.g. when the data are very sparse.

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© 2008 Zhejiang University Press, Hangzhou and Springer-Verlag GmbH Berlin Heidelberg

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(2008). Learning Locally and Globally: Maxi-Min Margin Machine. In: Machine Learning. Advanced Topics in Science and Technology in China. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79452-3_4

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  • DOI: https://doi.org/10.1007/978-3-540-79452-3_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-79451-6

  • Online ISBN: 978-3-540-79452-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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