DocumentCode
390010
Title
A new principle for measuring the generalization performance of SVMs
Author
Zhou, Weida ; Zhang, Li ; Jiao, Licheng
Author_Institution
Nat. Key Lab. for Radar Signal Process., Xidian Univ., Xi´´an, China
Volume
2
fYear
2002
fDate
26-30 Aug. 2002
Firstpage
1134
Abstract
A new method for estimating the VC dimension in advance is proposed, which can be taken as a principle for measuring the generalization performance of SVM. Our method adopts the two-order statistic of training samples and maintains consistency with the method for estimating the VC dimension in statistical learning theory. Our method can be applied to the problem of model selection and sample pre-processing. Simulation results show the feasibility and practicability of our method.
Keywords
generalisation (artificial intelligence); learning automata; learning by example; sampling methods; SVM; VC dimension estimation; generalization performance measurement; model selection; sample pre-processing; training samples; two-order statistic; Erbium; Neural networks; Pattern recognition; Performance evaluation; Radar signal processing; Signal processing algorithms; Statistical learning; Statistics; Support vector machines; Virtual colonoscopy;
fLanguage
English
Publisher
ieee
Conference_Titel
Signal Processing, 2002 6th International Conference on
Print_ISBN
0-7803-7488-6
Type
conf
DOI
10.1109/ICOSP.2002.1179989
Filename
1179989
Link To Document