Bounds on the rate of uniform convergence of learning processes with equality-expect noise samples on quasi-probability space

Author

Du, Er-ling ; Wang, Ying-xin ; Ha, Ming-Hu

Author_Institution

Great Wall Coll., China Univ. of Geosci., Baoding, China

Volume

3

fYear

2009

fDate

12-15 July 2009

Firstpage

1717

Lastpage

1722

Abstract

The bounds on the rate of uniform convergence of learning processes play an important role in the Statistical Learning Theory. They provide theoretical bases for the application of support vector machine and reflect the generalization ability of the learning machines. This paper mainly deals with the bounds on the rate of uniform convergence of learning processes when samples are corrupted by equality-expect noise on quasi-probability space.

Keywords

learning (artificial intelligence); probability; support vector machines; equality-expect noise samples; learning machines; learning processes uniform convergence; quasi-probability space; statistical learning theory; support vector machine; uniform convergence rate; Application software; Convergence; Cybernetics; Educational institutions; Geology; Machine learning; Mathematics; Petroleum; Probability; Statistical learning; Bounds on the rate of uniform convergence of learning processes; Equality-expect noise; Quasi-probability;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2009 International Conference on

Conference_Location

Baoding

Print_ISBN

978-1-4244-3702-3

Electronic_ISBN

978-1-4244-3703-0

Type

conf

DOI

10.1109/ICMLC.2009.5212267

Filename

5212267