中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
Posterior probability support vector machines for unbalanced data

文献类型：期刊论文

作者Tao, Q; Wu, GW; Wang, FY; Wang, J
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS
出版日期2005-11-01
卷号16期号:6页码:1561-1573
关键词Bayesian decision theory classification margin maximal margin algorithms v-SVM posterior probability support vector machines (SVMs) unbalanced data
ISSN号1045-9227
DOI10.1109/tnn.2005.857955
英文摘要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 nu is obtained which is similar to the nu-SVM developed by Scholkopf et al., and an empirical method for determining the posterior probability is proposed as a new approach to determine nu. The main advantage of an PPSVM classifier 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.
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000233350300021
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/10213]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Tao, Q
作者单位1.Chinese Acad Sci, Key Lab Complex Syst & Intelligence Sci, Inst Automat, Beijing 100080, Peoples R China
2.New Star Res Inst Appl Technol, Hefei 230031, Peoples R China
3.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Bioinformat Res Grp, Beijing 100080, Peoples R China
4.Univ Arizona, Tucson, AZ 85721 USA
推荐引用方式
GB/T 7714		 Tao, Q,Wu, GW,Wang, FY,et al. Posterior probability support vector machines for unbalanced data[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS,2005,16(6):1561-1573.
APA Tao, Q,Wu, GW,Wang, FY,&Wang, J.(2005).Posterior probability support vector machines for unbalanced data.IEEE TRANSACTIONS ON NEURAL NETWORKS,16(6),1561-1573.
MLA Tao, Q,et al."Posterior probability support vector machines for unbalanced data".IEEE TRANSACTIONS ON NEURAL NETWORKS 16.6(2005):1561-1573.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。