DocumentCode :
1748804
Title :
Learning with noise. Extension to regression
Author :
Teytaud, Olivier
Author_Institution :
CNRS, Bron, France
Volume :
3
fYear :
2001
fDate :
2001
Firstpage :
1787
Abstract :
Learning theory with noise provides an interesting framework. Outliers are a real-world problem. A simple model of outliers leads to similar conclusions than with much the difficult malicious errors; moreover, it sounds more realistic than constant noise, CPCN noise and malicious errors. The bias introduced by margin methods using distances to avoid NP-completeness can be a real problem and that asymptotic empirical risk minimization could be important
Keywords :
computational complexity; learning (artificial intelligence); learning automata; neural nets; noise; statistical analysis; NP-complete problem; learning with noise; malicious errors; neural nets; regression; support vector machine; Error analysis; Niobium; Polynomials; Risk management;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location :
Washington, DC
ISSN :
1098-7576
Print_ISBN :
0-7803-7044-9
Type :
conf
DOI :
10.1109/IJCNN.2001.938433
Filename :
938433
Link To Document :
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