Kernel classifier construction using orthogonal forward selection and boosting with Fisher ratio class separability measure.

Full Abstract

A greedy technique is proposed to construct parsimonious kernel classifiers using the orthogonal forward selection method and boosting based on Fisher ratio for class separability measure. Unlike most kernel classification methods, which restrict kernel means to the training input data and use a fixed common variance for all the kernel terms, the proposed technique can tune both the mean vector and diagonal covariance matrix of individual kernel by incrementally maximizing Fisher ratio for class separability measure. An efficient weighted optimization method is developed based on boosting to append kernels one by one in an orthogonal forward selection procedure. Experimental results obtained using this construction technique demonstrate that it offers a viable alternative to the existing state-of-the-art kernel modeling methods for constructing sparse Gaussian radial basis function network classifiers that generalize well.

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Author information

Author/s: Chen, S (S); Wang, X X (XX); Hong, X (X); Harris, C J (CJ);

Journal and publication information

Publication Type: Letter

Journal: IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council (IEEE Trans Neural Netw), published in United States. (Language: eng)

Reference: 2006-Nov; vol 17 (issue 6) : pp 1652-6

Dates: Created 2006/11/29; Completed 2006/12/21;

PMID: 17131680, status: MEDLINE (last retrieval date: 12/26/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Algorithms
Information Storage and Retrieval - methods
Neural Networks (Computer)

Neural Networks (Computer)
Pattern Recognition, Automated - methods
Signal Processing, Computer-Assisted

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