Learning to classify with missing and corrupted features

Proceedings of the 25th International Conference on Machine Learning

Volumn , Issue , 2008, Pages 216-223

  Dekel, Ofer ^a   Shamir, Ohad ^b  

^a MICROSOFT RESEARCH   (United States)

^b HEBREW UNIVERSITY OF JERUSALEM   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); LINEAR PROGRAMMING; MACHINE LEARNING; EDUCATION; LEARNING SYSTEMS; LINEARIZATION; ROBOT LEARNING;

BATCH TECHNIQUES; CLASSIFICATION TIME; LEARNING PROBLEM; MACHINE LEARNING TECHNIQUES; ONLINE LEARNING; REAL-WORLD SYSTEM; STATISTICAL LEARNING; STATISTICAL RISK;

LEARNING ALGORITHMS;

LEARNING PROBLEMS; MACHINE LEARNING ALGORITHMS; MACHINE-LEARNING TECHNIQUES; PERCEPTRON; REAL-WORLD SYSTEMS; STATISTICAL LEARNING; STATISTICAL RISKS; TRAINING DATUM;

EID: 56449100938     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1390156.1390184     Document Type: Conference Paper

References (12)

1. Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge University Press.
2. Carr R. D., & Lancia, G. (2000). Compact vs. exponential-size LP relaxations SANDIA Report 2000-2170.
3. Cesa-Bianchi, N., Conconi, A., & Gentile, C. (2004). On the generalization ability of on-line learning algorithms. IEEE Transactions on Information Theory, 50, 2050-2057.
4. Dietterich, T. G., & Bakiri, G. (1995). Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 2, 263-286.
5. Gamble, E., Macskassy, S., & Minton, S. (2007). Classification with pedigree and its applicability to record linkage. Workshop on Text-Mining & Link-Analysis.
6. Globerson, A., & Roweis, S. (2006). Nightmare at test time: robust learning by feature deletion. Proceedings of ICML 23 (pp. 353-360).
7. Joachims, T. (1998). Making large-scale support vector machine learning practical. In Advances in kernel methods - support vector learning. MIT Press.
8. Littlestone, N. (1991). Redundant noisy attributes, attribute errors, and linear-tlireshold learning using winnow. Proceedings of the COLT 4 (pp. 147-156).
9. McAllester, D. A. (2003). Simplified PAC-bayesian margin bounds. Proceedings of COLT 16 (pp. 203-215).
10. Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65, 386-407.
11. Teo, C.-H., Globerson, A., Roweis, S., & Smola, A. (2008). Convex learning with invariances. Advances in NIPS 21.
12. Vapnik, V. N. (1998). Statistical learning theory. Wiley.