期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 24, 期 8, 页码 1304-1315出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2013.2250300
关键词
Active set method; feasibility analysis; finite convergence analysis; incremental nu-support vector classification; online learning
类别
资金
- Priority Academic Program Development of Jiangsu Higher Education Institutions
- National Science Foundation [IIS-1115417]
- National Natural Science Foundation of China [61232016, 61202137]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1115417] Funding Source: National Science Foundation
The nu-support vector machine (nu-SVM) for classification has the advantage of using a parameter nu on controlling the number of support vectors and margin errors. Recently, an interesting accurate on-line algorithm accurate on-line nu-SVM algorithm (AONSVM) is proposed for training nu-SVM. AONSVM can be viewed as a special case of parametric quadratic programming techniques. It is demonstrated that AONSVM avoids the infeasible updating path as far as possible, and successfully converges to the optimal solution based on experimental analysis. However, because of the differences between AONSVM and classical parametric quadratic programming techniques, there is no theoretical justification for these conclusions. In this paper, we prove the feasibility and finite convergence of AONSVM under two assumptions. The main results of feasibility analysis include: 1) the inverses of the two key matrices in AONSVM always exist; 2) the rules for updating the two key inverse matrices are reliable; 3) the variable zeta can control the adjustment of the sum of all the weights efficiently; and 4) a sample cannot migrate back and forth in successive adjustment steps among the set of margin support vectors, the set of error support vectors, and the set of the remaining vectors. Moreover, the analyses of AONSVM also provide the proofs of the feasibility and finite convergence for accurate on-line C-SVM learning directly.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据