Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/21398
Title: Iteratively reweighted fitting for reduced multivariate polynomial model
Authors: Zuo, W
Wang, K
Zhang, D 
Yue, F
Issue Date: 2007
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2007, v. 4492 LNCS, no. PART 2, p. 583-592 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: Recently a class of reduced multivariate polynomial models (RM) has been proposed that performs well in classification tasks involving few features and many training data. The RM method, however, adopts a ridge leastsquare estimator, overlooking the fact that least square error usually does not correspond to minimum classification error. In this paper, we propose an iteratively reweighted regression method and two novel weight functions for fitting the RM model (IRF-RM). The IRF-RM method iteratively increases the weights of samples prone to misclassification and decreases the weights of samples far from the decision boundary, making the IRF-RM model more suitable for efficient pattern classification. A number of benchmark data sets are used to evaluate the IRF-RM method. Experimental results indicate that IRF-RM achieves a higher or comparable classification accuracy compared with RM and several state-of-the-art classification approaches.
Description: 4th International Symposium on Neural Networks, ISNN 2007, Nanjing, 3-7 June 2007
URI: http://hdl.handle.net/10397/21398
ISBN: 9783540723929
ISSN: 0302-9743
EISSN: 1611-3349
Appears in Collections:Conference Paper

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