NDLI: Design of SVM based on radial basis function neural networks pre-partition

Content Provider	IEEE Xplore Digital Library
Author	Guan, Lixin Xie, Weixin Pei, Jihong
Copyright Year	2014
Description	Author affiliation: ATR Key Laboratory of National Defense Technology, Shenzhen University, China (Guan, Lixin; Xie, Weixin; Pei, Jihong)
Abstract	In order to solve the training time problem of the support vector machine for a large dataset, in this paper, an alternative approach motivated by the radial basis function neural network is developed to partition the subset of SVs for the SVM. The proposed method aims at obtain an optimal decision boundary based on the RBFNN, because it has good convergence and fast training. On the other hand, the method concerns on extracting a candidate set of the SVs from a large dataset using the decision boundary. The data structure of the RBFNN and SVM with Gaussian kernels is consistent in high dimensional feature space, moreover, the RBFNN was used to approximate any continuous nonlinear function, therefore, the subset can model the characteristics of the support vectors for a large dataset, and it is worth noting that the size of the subset is far smaller than the original training set. Experimental results show that the proposed method improves the performance of the SVM.
Starting Page	1480
Ending Page	1483
File Size	723950
Page Count	4
File Format	PDF
ISBN	9781479921881
ISSN	21645221
e-ISBN	9781479921867
DOI	10.1109/ICOSP.2014.7015245
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Vectors Accuracy Kernel Clustering algorithms Memory management subset support vector machine (SVM) radial basis function neural network (RBFNN) pre-partition
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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