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
Link To Document :
بازگشت