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

Chen, S., Wang, X. X., Hong, X. and Harris, C. J. (2006) Kernel classifier construction using orthogonal forward selection and boosting with Fisher ratio class separability measure. IEEE Transactions on Neural Networks, 17 (6). pp. 1652-1656. ISSN 1045-9227

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To link to this item DOI: 10.1109/tnn.2006.881487

Abstract/Summary

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.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:15175
Uncontrolled Keywords:boosting, classification, Fisher ratio of class separability, forward, selection, kernel classifier, orthogonal least square, radial basis, function network

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