DocumentCode
2891216
Title
The Key Theorem of Learning Theory with Samples Corrupted by Equality-Expect Noise on Quasi-Probability Space
Author
Ha, Ming-Hu ; Du, Er-ling ; Feng, Zhi-fang ; Bai, Yun-Chao
Author_Institution
Coll. of Math. & Comput. Sci., Hebei Univ., Baoding
fYear
2006
fDate
13-16 Aug. 2006
Firstpage
1784
Lastpage
1789
Abstract
Based on statistical learning theory on probability space and good properties of quasi-probability, some important inequalities are proven on quasi-probability space in this paper. Furthermore, some new concepts of learning theory are given and the key theorem of statistical learning theory is given and proven when samples are corrupted by equality-expect noise on quasi-probability space
Keywords
learning (artificial intelligence); probability; statistical analysis; equality-expect noise; quasiprobability space; statistical learning theory; Additives; Chebyshev approximation; Cybernetics; Density functional theory; Distribution functions; Educational institutions; Machine learning; Mathematics; Probability; Random variables; Statistical learning; Quasi-probability; equality-expect noise; the empirical risk functional; the expected risk functional; the key theorem;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location
Dalian, China
Print_ISBN
1-4244-0061-9
Type
conf
DOI
10.1109/ICMLC.2006.258981
Filename
4028354
Link To Document