Texture Classification Using Kernel-Based Techniques

被引：0

作者：

Fernandez-Lozano, Carlos ^{[1
]}

Seoane, Jose A. ^{[2
]}

Gestal, Marcos ^{[1
]}

Gaunt, Tom R. ^{[2
]}

Campbell, Cohn ^{[3
]}

机构：

[1] Univ A Coruna, Informat & Commun Technol Dept, Fac Comp Sci, Campus Elvina S-N, La Coruna 15071, Spain

[2] Univ Bristol, MRC Ctr Causal Anal Translat Epidemiol, Sch Social & Commun Med, Bristol BS8 2BN, Avon, England

[3] Univ Bristol, Dept Engn Math, Bristol BS81UB, Avon, England

来源：

ADVANCES IN COMPUTATIONAL INTELLIGENCE, PT I | 2013年 / 7902卷

基金：

英国医学研究理事会;

关键词：

Multiple Kernel Learning; Support Vector Machines; Recursive Feature Elimination; Genetic Algorithms; SUPPORT VECTOR MACHINES; SELECTION; FEATURES;

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：

In this paper, a high-dimensional textural heterogenous dataset is evaluated. This problem should be studied with specific techniques or a solution for decreasing dimensionality should be applied in order to improve the classification results. Thus, this problem is tackled by means of three differente techniques: an specific technique such as Multiple Kernel Learning, and two different feature selection techniques such as Support Vector Machines-Recursive Feature Elimination and a Genetic Algorithm-based approaches. We found that the best technique is Support Vector Machines-Recursive Feature Elimination, with a AUROC score of 92,45%.

引用

页码：427 / +

页数：3

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