4.6 Article

Robust relevance vector machine for classification with variational inference

期刊

ANNALS OF OPERATIONS RESEARCH
卷 263, 期 1-2, 页码 21-43

出版社

SPRINGER
DOI: 10.1007/s10479-015-1890-9

关键词

Relevance vector machine; Outlier; Robust classification; Sparsity

向作者/读者索取更多资源

The relevance vector machine (RVM) is a widely employed statistical method for classification, which provides probability outputs and a sparse solution. However, the RVM can be very sensitive to outliers far from the decision boundary which discriminates between two classes. In this paper, we propose the robust RVM based on a weighting scheme, which is insensitive to outliers and simultaneously maintains the advantages of the original RVM. Given a prior distribution of weights, weight values are determined in a probabilistic way and computed automatically during training. Our theoretical result indicates that the influences of outliers are bounded through the probabilistic weights. Also, a guideline for determining hyperparameters governing a prior is discussed. The experimental results from synthetic and real data sets show that the proposed method performs consistently better than the RVM if a training data set is contaminated by outliers.

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。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据