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