DocumentCode
527345
Title
The key theorem of learning theory based on the rough fuzzy samples
Author
Wang, Xiao-li ; Tian, Da-Zeng ; Huang, Shu
Author_Institution
Coll. of Math. & Comput. Sci., Hebei Univ., Baoding, China
Volume
1
fYear
2010
fDate
11-14 July 2010
Firstpage
314
Lastpage
318
Abstract
Firstly, the Khinchine law of large numbers based on the rough fuzzy samples is given. Secondly, based on the rough fuzzy samples, some concepts such as rough fuzzy expected risk functional, rough fuzzy empirical risk functional and rough fuzzy empirical risk minimization principle are proposed. Finally, the key theorem of learning theory based on the rough fuzzy sample is proved.
Keywords
fuzzy set theory; learning (artificial intelligence); rough set theory; statistical analysis; Khinchine law; learning theory; rough fuzzy samples; Chromium; Convergence; Cybernetics; Radio frequency; Risk management; Statistical learning; Empirical risk functional; Empirical risk minimization principle; Expected risk functional; Rough fuzzy variable; The key theorem;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics (ICMLC), 2010 International Conference on
Conference_Location
Qingdao
Print_ISBN
978-1-4244-6526-2
Type
conf
DOI
10.1109/ICMLC.2010.5581044
Filename
5581044
Link To Document