.png){kind=icon}
返回
Sparse learning for support vector classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2010.06.017.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper provides a sparse learning algorithm for Support Vector Classification (SVC), called Sparse Support Vector Classification (SSVC), which leads to sparse solutions by automatically setting the irrelevant parameters exactly to zero. SSVC adopts the L-O-norm regularization term and is trained by an iteratively reweighted learning algorithm. We show that the proposed novel approach contains a hierarchical-Bayes interpretation. Moreover, this model can build up close connections with some other sparse models. More specifically, one variation of the proposed method is equivalent to the zero-norm classifier proposed in (Weston et al., 2003): it is also an extended and more flexible framework in parallel with the Sparse Probit Classifier proposed by Figueiredo (2003). Theoretical justifications and experimental evaluations on two synthetic datasets and seven benchmark datasets show that SSVC offers competitive performance to SVC but needs significantly fewer Support Vectors. (C) 2010 Elsevier B.V. All rights reserved.
Keyword:
Sparse representation
Implementations of L-O-norm
Regularization term
Support vector machine
Kernel methods
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.3
论文数:
8.0K
被引数:
1.6W
机构
引用论文
On the Development and Applications of Cellulosic Nanofibrillar and Nanocrystalline Materials纤维素纳米原纤和纳米晶材料的发展与应用
PREAI
分享收藏
A new method for sparsity control in support vector classification and regression.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏
查看更多内容