Learning generalization bound with an application to sparse-representation classifiers

Machine Learning

Volumn 42, Issue 3, 2001, Pages 233-239

  Gat, Yoram ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ERROR ANALYSIS; LEARNING SYSTEMS; NEURAL NETWORKS; PATTERN RECOGNITION SYSTEMS; PROBABILITY; PROBLEM SOLVING; THEOREM PROVING; VECTORS;

GENERALIZATION ABILITY; PERCEPTION ALGORITHM; SUPPORT VECTOR MACHINES;

LEARNING ALGORITHMS;

EID: 0035283216     PISSN: 08856125     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1007605716762     Document Type: Article

References (6)

1. Devroye, L. (1982). Bounds for the uniform deviation of empirical measures. Journal of Multivariate Analysis, 12, 72-79.
2. Floyd, S. & Warmuth M. (1995). Sample compression, learnability, and the Vapnik-Chervonenekis dimension. Machine Learning Journal, 21, 269-304.
3. Freund, Y. & Schapire, R. E. (1998). Large margin classification using the perception algorithm. In COLT '98: Proceedings of the Eleventh Annual Conference on Computational Learning Theory.
4. Graepel, T., Herbrich, R., & Shawe-Taylor, J. (2000). Generalisation error bounds for sparse linear classifiers. In COLT 2000: Proceedings of the Thirteenth Annual Conference on Computational Learning Theory.
5. Minsky, M. L. & Papert, S. A. (1988). Perceptrons. The MIT Press, Cambridge, MA.
6. Vapnik, V. N. (1998). Statistical Learning Theory. John Wiley & Sons, Inc., New York City, NY.