NDLI: A Novel Multi-Reduced Support Vector Machine

Content Provider	IEEE Xplore Digital Library
Author	Fangfang Wu Yinliang Zhao
Copyright Year	2005
Description	Author affiliation: Inst. of Neocomputer, Xi'an Jiaotong Univ. (Fangfang Wu)
Abstract	For solving the problem that support vector machine (SVM) is restricted to work well on the small sample sets, a novel multi-reduced support vector machine (MRSVM) is proposed. MRSVM has two reduced steps. Firstly, a novel reduced support vector machine on density clustering (RSVM-DC) is proposed. This algorithm focuses on dealing with a sample set through density clustering prior to classifying the samples. After clustering the positive samples and negative samples, the algorithm picks out such samples that locate on the edge of clusters as reduced samples. These reduced samples are treated as the new training sample set used in SVM's classifier system. Additionally, it can also improve precision by reducing the percentage of counterexamples in kernel object epsi-area. Secondly, a novel reduced algorithm in high dimension through kernel mapping is proposed. Based on the linear classified character in high dimension, it can select the edge samples of each kind sample set to reduce the kernel computing times. Experiment results show that not only efficiency but also classification precision are improved by MRSVM, compared with other reduced algorithms
Starting Page	322
Ending Page	326
File Size	2364020
Page Count	5
File Format	PDF
ISBN	0780394224
DOI	10.1109/ICNNB.2005.1614624
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Handwriting recognition Image recognition Machine learning algorithms Face recognition Support vector machine classification Clustering algorithms Machine learning Kernel Statistics
Content Type	Text
Resource Type	Article

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