Sex with Support Vector Machines

被引：0
作者：
Moghaddam, B [1 ]
Yang, MH [1 ]
机构：
[1] Mitsubishi Elect Res Lab, Cambridge, MA 02139 USA
来源：
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 13 | 2001年 / 13卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Nonlinear Support Vector Machines (SVMs) are investigated for visual sex classification with low resolution "thumbnail" faces (21-by-12 pixels) processed from 1,755 images from the FERET face database. The performance of SVMs is shown to be superior to traditional pattern classifiers (Linear, Quadratic, Fisher Linear Discriminant, Nearest-Neighbor) as well as more modern techniques such as Radial Basis Function (RBF) classifiers and large ensemble-RBF networks. Furthermore, the SVM performance (3.4% error) is currently the best result reported in the open literature.
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收藏
页码：960 / 966
页数：7
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