DocumentCode
424149
Title
The key theorem of learning theory about examples corrupted by noise
Author
Ha, Ming-Hu ; Li, Jun-Hua ; Li, Jia ; Wang, Xi-Zhao
Author_Institution
Coll. of Math. & Comput. Sci., Hebei Univ., Baoding, China
Volume
3
fYear
2004
fDate
26-29 Aug. 2004
Firstpage
1911
Abstract
Statistical learning theory has investigated the conditions for consistency of the learning processes based on the empirical risk minimization induction principle. However, it deals with the unrealistic, i.e. noise-free case. We give the key theorem when the outputs are corrupted by noise.
Keywords
learning (artificial intelligence); minimisation; random noise; statistical analysis; empirical risk minimization induction principle; learning processes; noise corruption; statistical learning theory; Convergence; Cybernetics; Educational institutions; Machine learning; Probability distribution; Random variables; Risk management; Statistical learning; Sufficient conditions;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN
0-7803-8403-2
Type
conf
DOI
10.1109/ICMLC.2004.1382091
Filename
1382091
Link To Document