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.

引用

页码：960 / 966

页数：7

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