DocumentCode
2305744
Title
The Key Theorem of Statistical Learning Theory with Rough Samples
Author
Liu, Yang ; Dong, Kai-kun ; Guo, Li ; Yuan, Xing-Ling
Author_Institution
Harbin Inst. of Technol. at Weihai, Weihai, China
Volume
4
fYear
2009
fDate
19-21 May 2009
Firstpage
543
Lastpage
547
Abstract
A key theorem of statistical learning theory with rough samples is proposed. The theorem provides a theoretical basis for the applied research of supporting vector machine etc. and therefore plays an important role in statistical learning theory. In view of the uncertainty of the real world, this paper combines the trust theory and statistical learning theory to generalize the key theorem of learning theory. Random samples are replaced with rough samples and rough empirical risk minimization principle is proposed. The theorem is proven in detail.
Keywords
learning (artificial intelligence); statistical analysis; support vector machines; key theorem; rough empirical risk minimization principle; rough samples; statistical learning theory; supporting vector machine; trust theory; Machine learning; Mathematics; Pattern recognition; Risk management; Software engineering; Statistical learning; Statistics; Support vector machines; Turning; Uncertainty; Trust theory; rough empirical risk minimization principle; the key theorem;
fLanguage
English
Publisher
ieee
Conference_Titel
Software Engineering, 2009. WCSE '09. WRI World Congress on
Conference_Location
Xiamen
Print_ISBN
978-0-7695-3570-8
Type
conf
DOI
10.1109/WCSE.2009.23
Filename
5319619
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