DocumentCode
578168
Title
The key theorem of learning theory with samples corrupted by zero-expect noise on chance space
Author
Li, Jun-hua ; Li, Hai-jun ; He, Qiang
Author_Institution
Coll. of Math. & Comput. Sci., Hebei Univ., Baoding, China
Volume
2
fYear
2012
fDate
15-17 July 2012
Firstpage
797
Lastpage
800
Abstract
Statistical learning theory has been regarded as one of the best theories for solving the small-sample learning problem on probability space. But it was difficult to deal with the problems on non-probability spaces and it mainly dealt with the noise-free case. In this paper, the key theorem with samples corrupted by zero-expect noise on chance space will be given and proven.
Keywords
learning (artificial intelligence); probability; chance space; noise-free case; nonprobability spaces; small-sample learning problem; statistical learning theory; zero-expect noise; Abstracts; Erbium; Integrated circuits; World Wide Web; Chance space; Hybrid empirical risk minimization principle; Hybrid variable; Key theorem;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics (ICMLC), 2012 International Conference on
Conference_Location
Xian
ISSN
2160-133X
Print_ISBN
978-1-4673-1484-8
Type
conf
DOI
10.1109/ICMLC.2012.6359027
Filename
6359027
Link To Document