Gender Classification Using an NFS-SVM Classifier

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Please use this identifier to cite or link to this item: http://chur.chu.edu.tw/handle/987654321/31286

Title:	Gender Classification Using an NFS-SVM Classifier
Authors:	李建興 Lee, Chang-Hsing
Contributors:	資訊工程學系 Computer Science & Information Engineering
Keywords:	Support vector machine;eigenspace projection;gender classification;class scatter matrix
Date:	2013
Issue Date:	2014-06-27 01:25:09 (UTC+8)
Abstract:	SVM and eigenspace projection methods are widely used in pattern recognition. A separating hyperplane and the projection axes are found for solving the two-class classification problem. The covariance matrices always represent the class scatters. In this
Appears in Collections:	[Department of Computer Science and Information Engineering] Journal Articles

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