Title :
Feature selection for linear support vector machines
Author :
Liang, Zhizheng ; Zhao, Tuo
Author_Institution :
Dept. of Comput., Shenzhen Graduate Sch.
Abstract :
Feature selection is attracted much interest from researchers in many fields such as pattern recognition and data mining. In this paper, a novel algorithm for feature selection is developed. The proposed algorithm uses the standard linear SVM algorithm and is performed in an iterative way. Feature selection is carried out by assigning weights to features. Experimental results on UCI data set and face images confirm the feasibility and validation of the proposed method
Keywords :
face recognition; feature extraction; support vector machines; face images; feature selection; linear support vector machines; Data mining; Equations; Feature extraction; Iterative algorithms; Kernel; Machine learning; Pattern recognition; Statistics; Support vector machine classification; Support vector machines;
Conference_Titel :
Pattern Recognition, 2006. ICPR 2006. 18th International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
0-7695-2521-0
DOI :
10.1109/ICPR.2006.560
