期刊
NEURAL COMPUTING & APPLICATIONS
卷 19, 期 8, 页码 1197-1206出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00521-010-0358-8
关键词
Semi-supervised learning; Genetic algorithm; Support vector machine; SVM
资金
- NSERC of Canada
The Support Vector Machine (SVM) is an interesting classifier with excellent power of generalization. In this paper, we consider applying the SVM to semi-supervised learning. We propose using an additional criterion with the standard formulation of the semi-supervised SVM (S (3) VM) to reinforce classifier regularization. Since, we deal with nonconvex and combinatorial problem, we use a genetic algorithm to optimize the objective function. Furthermore, we design the specific genetic operators and certain heuristics in order to improve the optimization task. We tested our algorithm on both artificial and real data and found that it gives promising results in comparison with classical optimization techniques proposed in literature.
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