期刊
INFORMATION PROCESSING & MANAGEMENT
卷 45, 期 6, 页码 631-642出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.ipm.2009.05.005
关键词
SVM; Kernels; Multinomial dirichlet; Finite mixture models; Maximum likelihood; EM; CEMM; Deterministic annealing; MDL; Spam; Image database
资金
- Natural Sciences and Engineering Research Council of Canada (NSERC)
- NATEQ Nouveaux Chercheurs
- Concordia University
In this paper, we investigate the problem of training support vector machines (SVMs) on count data. Multinomial Dirichlet mixture models allow us to model efficiently count data. On the other hand, SVMs permit good discrimination. We propose, then, a hybrid model that appropriately combines their advantages. Finite mixture models are introduced, as an SVM kernel, to incorporate prior knowledge about the nature of data involved in the problem at hand. For the learning of our mixture model, we propose a deterministic annealing component-wise EM algorithm mixed with a minimum description length type criterion. In the context of this model, we compare different kernels. Through some applications involving spam and image database categorization, we find that our data-driven kernel performs better. (C) 2009 Elsevier Ltd. All rights reserved.
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