Robust bounds for classification via selective sampling

Proceedings of the 26th International Conference On Machine Learning, ICML 2009

Volumn , Issue , 2009, Pages 121-128

  Cesa Bianchi, Nicolò ^a   Gentile, Claudio ^b   Orabona, Francesco ^c  

^a UNIVERSITY OF MILAN   (Italy)

^b UNIVERSITY OF INSUBRIA   (Italy)

^c IDIAP RESEARCH INSTITUTE   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

BASE CLASSIFIERS; BIAS AND VARIANCE; BINARY CLASSIFICATION; NOISE MODELS; OPTIMAL CLASSIFIERS; PERFORMANCE BOUNDS; REGULARIZED LEAST SQUARES; ROBUST BOUND; SAMPLING CONDITIONS; SAMPLING STRATEGIES; SELECTIVE SAMPLING; SEMI-SUPERVISED ALGORITHM; STANDARD RATES; UPPER BOUND;

CLASSIFIERS; ROBOT LEARNING;

ALGORITHMS;

EID: 71149102767     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Auer, P. (2002). Using confidence bounds for exploitation-exploration trade-offs. Journal of Machine Learning Research, 3, 397-422.
2. Balcan, M., Beygelzimer, A., & Langford, J. (2006). Agnostic active learning. Proc. of the 23rd International Conference on Machine Learning (pp. 65-72).
3. Balcan, M., Broder, A., & Zhang, T. (2007). Margin-based active learning. Proceedings of the 20th Annual Conference on Learning Theory (pp. 35-50).
4. Cavallanti, G., Cesa-Bianchi, N., & Gentile, C. (2007). Tracking the best hyperplane with a simple budget Perceptron. Machine Learning, 69, 143-167.
5. Cavallanti, G., Cesa-Bianchi, N., & Gentile, C. (2009). Linear classification and selective sampling under low noise conditions. In Advances in Neural Information Processing Systems 21 (pp. 249-256).
6. Cesa-Bianchi, N., Gentile, C., & Zaniboni, L. (2006a). Incremental algorithms for hierarchical classification. Journal of Machine Learning Research, 7, 31-54.
7. Cesa-Bianchi, N., Gentile, C., & Zaniboni, L. (2006b). Worst-case analysis of selective sampling for linear classification. Journal of Machine Learning Research, 7, 1025-1230.
8. Cohn, R., Atlas, L., & Ladner, R. (1990). Training connectionist networks with queries and selective sampling. In Advances in Neural Information Processing Systems 2 (pp. 566-573).
9. Dasgupta, S., Hsu, D., & Monteleoni, C. (2008). A general agnostic active learning algorithm. In Advances in Neural Information Processing Systems 21 (pp. 353-360).
10. Dasgupta, S., Kalai, A. T., & Monteleoni, C. (2005). Analysis of perceptron-based active learning. Proceedings of the 18th Annual Conference on Learning Theory (pp. 249-263).
11. Dekel, O., Shalev-Shwartz, S., & Singer, Y. (2007). The Forgetron: A kernel-based Perceptron on a budget. SIAM Journal on Computing, 37, 1342-1372.
12. Freund, Y., Seung, S., Shamir, E., & Tishby, N. (1997). Selective sampling using the query by committee algorithm. Machine Learning, 28, 133-168.
13. Li, L., Littman, M., & Walsh, T. (2008). Knows what it knows: a framework for self-aware learning. Proceedings of the 25th International Conference on Machine Learning (pp. 568-575).
14. Orabona, F., Keshet, J., & Caputo, B. (2008). The Projectron: a bounded kernel-based Perceptron. Proceedings of the 25th International Conference on Machine Learning (pp. 720-727).
15. Strehl, A., & Littman, M. (2008). Online linear regression and its application to model-based reinforcement learning. In Advances in Neural Information Processing Systems 20 (pp. 631-638).
16. Weston, J., Bordes, A., & Bottou, L. (2005). Online (and offline) on an even tighter budget. Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics (pp. 413-420).