On L1-norm multi-class support vector machines

Lifeng Wang, Xiaotong T Shen, Yuan F. Zheng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Binary Support Vector Machines (SVM) have proven effective in classification. However, problems remain with respect to feature selection in multi-class classification. This article proposes a novel multi-class SVM, which performs classification and feature selection simultaneously via Li-norm penalized sparse representations. The proposed methodology, together with our developed regularization solution path, permits feature selection within the framework of classification. The operational characteristics of the proposed methodology is examined via both simulated and benchmark exampies, and is compared to some competitors in terms of the accuracy of prediction and feature selection. The numerical results suggest that the proposed methodology is highly competitive.

Original languageEnglish (US)
Title of host publicationProceedings - 5th International Conference on Machine Learning and Applications, ICMLA 2006
Pages83-88
Number of pages6
DOIs
StatePublished - Dec 1 2006
Event5th International Conference on Machine Learning and Applications, ICMLA 2006 - Orlando, FL, United States
Duration: Dec 14 2006Dec 16 2006

Other

Other5th International Conference on Machine Learning and Applications, ICMLA 2006
CountryUnited States
CityOrlando, FL
Period12/14/0612/16/06

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Wang, L., Shen, X. T., & Zheng, Y. F. (2006). On L1-norm multi-class support vector machines. In Proceedings - 5th International Conference on Machine Learning and Applications, ICMLA 2006 (pp. 83-88). [4041474] https://doi.org/10.1109/ICMLA.2006.38