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Kernel Maximum a Posteriori Classification with Error Bound Analysis

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Neural Information Processing (ICONIP 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4984))

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Abstract

Kernel methods have been widely used in data classification. Many kernel-based classifiers like Kernel Support Vector Machines (KSVM) assume that data can be separated by a hyperplane in the feature space. These methods do not consider the data distribution. This paper proposes a novel Kernel Maximum A Posteriori (KMAP) classification method, which implements a Gaussian density distribution assumption in the feature space and can be regarded as a more generalized classification method than other kernel-based classifier such as Kernel Fisher Discriminant Analysis (KFDA). We also adopt robust methods for parameter estimation. In addition, the error bound analysis for KMAP indicates the effectiveness of the Gaussian density assumption in the feature space. Furthermore, KMAP achieves very promising results on eight UCI benchmark data sets against the competitive methods.

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Masumi Ishikawa Kenji Doya Hiroyuki Miyamoto Takeshi Yamakawa

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© 2008 Springer-Verlag Berlin Heidelberg

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Xu, Z., Huang, K., Zhu, J., King, I., Lyu, M.R. (2008). Kernel Maximum a Posteriori Classification with Error Bound Analysis. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4984. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69158-7_87

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  • DOI: https://doi.org/10.1007/978-3-540-69158-7_87

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69154-9

  • Online ISBN: 978-3-540-69158-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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