Title :
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
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;
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
DOI :
10.1109/ICMLC.2009.5212267
