Posterior probability support vector Machines for unbalanced data Neural Networks

CASIA OpenIR > 09年以前成果

	Posterior probability support vector Machines for unbalanced data Neural Networks
	Qing Tao; Gao-Wei Wu; Fei-Yue Wang; Jue Wang
发表期刊	IEEE Transactions on Neural Networks
	2005-11
卷号	16 期号:6 页码:1561-1573
摘要	This paper proposes a complete framework of posterior probability support vector machines (PPSVMs) for weighted training samples using modified concepts of risks, linear separability, margin, and optimal hyperplane. Within this framework, a new optimization problem for unbalanced classification problems is formulated and a new concept of support vectors established. Furthermore, a soft PPSVM with an interpretable parameter V is obtained which is similar to the -SVM developed by Schölkopf et al., and an empirical method for determining the posterior probability is proposed as a new approach to determine . The main advantage of anPPSVMclassifier lies in that fact that it is closer to the Bayes optimal without knowing the distributions. To validate the proposed method, two synthetic classification examples are used to illustrate the logical correctness of PPSVMs and their relationship to regular SVMs and Bayesian methods. Several other classification experiments are conducted to demonstrate that the performance of PPSVMs is better than regular SVMs in some cases. Compared with fuzzy support vector machines (FSVMs), the proposed PPSVM is a natural and an analytical extension of regular SVMs based on the statistical learning theory.
关键词	Bayesian Decision Theory Classification Margin Maximal Margin Algorithms -svm Posterior Probability Support Vector Machines (Svms) Unbalanced Data.
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/14885
专题	09年以前成果
推荐引用方式 GB/T 7714	Qing Tao,Gao-Wei Wu,Fei-Yue Wang,et al. Posterior probability support vector Machines for unbalanced data Neural Networks[J]. IEEE Transactions on Neural Networks,2005,16(6):1561-1573.
APA	Qing Tao,Gao-Wei Wu,Fei-Yue Wang,&Jue Wang.(2005).Posterior probability support vector Machines for unbalanced data Neural Networks.IEEE Transactions on Neural Networks,16(6),1561-1573.
MLA	Qing Tao,et al."Posterior probability support vector Machines for unbalanced data Neural Networks".IEEE Transactions on Neural Networks 16.6(2005):1561-1573.