Robust 1-norm soft margin smooth support vector machine

Li Jen Chien*, Yuh-Jye Lee, Zhi Peng Kao, Chih Cheng Chang

*此作品的通信作者

研究成果: Conference contribution同行評審

4 引文 斯高帕斯(Scopus)

摘要

Based on studies and experiments on the loss term of SVMs, we argue that 1-norm measurement is better than 2-norm measurement for outlier resistance. Thus, we modify the previous 2-norm soft margin smooth support vector machine (SSVM 2) to propose a new 1-norm soft margin smooth support vector machine (SSVM 1). Both SSVMs can be solved in primal form without a sophisticated optimization solver. We also propose a heuristic method for outlier filtering which costs little in training process and improves the ability of outlier resistance a lot. The experimental results show that SSVM 1 with outlier filtering heuristic performs well not only on the clean, but also the polluted synthetic and benchmark UCI datasets.

原文English
主出版物標題Intelligent Data Engineering and Automated Learning, IDEAL 2010 - 11th International Conference, Proceedings
頁面145-152
頁數8
DOIs
出版狀態Published - 8 11月 2010
事件11th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2010 - Paisley, United Kingdom
持續時間: 1 9月 20103 9月 2010

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
6283 LNCS
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Conference

Conference11th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2010
國家/地區United Kingdom
城市Paisley
期間1/09/103/09/10

指紋

深入研究「Robust 1-norm soft margin smooth support vector machine」主題。共同形成了獨特的指紋。

引用此