DocumentCode :
3275083
Title :
The bounds on the risk for sets of unbounded nonnegative functions on possibility space
Author :
Wang, Peng ; Zhang, Chun-qin
Author_Institution :
Coll. of Phys. Sci. & Technol., Hebei Univ., Baoding, China
Volume :
2
fYear :
2011
fDate :
10-13 July 2011
Firstpage :
881
Lastpage :
886
Abstract :
Statistical learning theory on probability space is an important part of Machine Learning. Based on the key theorem, the bounds of uniform convergence have significant meaning. These bounds determine generalization ability of the learning machines utilizing the empirical risk minimization induction principle. In this paper, the bounds on the risk for sets of unbounded nonnegative functions on possibility space are discussed, and the rate of uniform convergence is estimated.
Keywords :
generalisation (artificial intelligence); learning (artificial intelligence); statistical analysis; empirical risk minimization induction principle; generalization ability; machine learning; possibility space; probability space; statistical learning theory; unbounded nonnegative functions; uniform convergence; Convergence; Cybernetics; Educational institutions; Random variables; Statistical learning; Credibility measure; Possibility space; The bounds on the risk for unbounded nonnegative functions; The empirical risk; The expected risk;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2011 International Conference on
Conference_Location :
Guilin
ISSN :
2160-133X
Print_ISBN :
978-1-4577-0305-8
Type :
conf
DOI :
10.1109/ICMLC.2011.6016825
Filename :
6016825
Link To Document :
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