Linear classification and selective sampling under low noise conditions

Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

Volumn , Issue , 2009, Pages 249-256

  Cavallanti, Giovanni ^a   Cesa Bianchi, Nicolò ^a   Gentile, Claudio ^b  

^a UNIVERSITY OF MILAN   (Italy)

^b UNIVERSITY OF INSUBRIA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

RISK ASSESSMENT;

AVERAGE RISK; BAYES RISK; CONVERGENCE RATES; EXPONENTIALS; LINEAR CLASSIFICATION; LOWER NOISE; NOISE CONDITIONS; SELECTIVE SAMPLING; SIMPLE++; TEXTUAL DATA;

ARTIFICIAL INTELLIGENCE;

EID: 70049109273     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. D. Angluin. Queries revisited. In 12th ALT, pages 12-31. Springer, 2001. (Pubitemid 33371210)
2. K.S. Azoury and M.K.Warmuth. Relative loss bounds for on-line density estimation with the exponential family of distributions. Machine Learning, 43(3):211-246, 2001. (Pubitemid 32471833)
3. M.F. Balcan, A. Beygelzimer, and J. Langford. Agnostic active learning. In 23rd ICML, pages 65-72. ACM Press, 2006.
4. M.F. Balcan, A. Broder, and T. Zhang. Margin-based active learning. In 20th COLT, pages 35-50. Springer, 2007.
5. P.L. Bartlett, M.I. Jordan, and J.D. McAuliffe. Convexity, classification, and risk bounds. JASA, 101(473):138-156, 2006. (Pubitemid 43500031)
6. G. Blanchard, O. Bousquet, and L. Zwald. Statistical properties of kernel principal component analysis. Machine Learning, 66:259-294, 2007. (Pubitemid 46360614)
7. M.L. Braun. Accurate error bounds for the eigenvalues of the kernel matrix. JMLR, 7:2303-2328, 2006. (Pubitemid 44708008)
8. C. Campbell, N. Cristianini, and A. Smola. Query learning with large margin classifiers. In 17th ICML, pages 111-118. Morgan Kaufmann, 2000.
9. R. Castro and R.D. Nowak. Minimax bounds for active learning. IEEE Trans. IT, 2008. To appear.
10. N. Cesa-Bianchi, A. Conconi, and C. Gentile. Learning probabilistic linear-threshold classifiers via selective sampling. In 16th COLT, pages 373-387. Springer, 2003. (Pubitemid 37053217)
11. N. Cesa-Bianchi, C. Gentile, and L. Zaniboni. Worst-case analysis of selective sampling for linear classification. JMLR, 7:1205-1230, 2006. (Pubitemid 44015299)
12. N. Cesa-Bianchi and G. Lugosi. Prediction, Learning, and Games. Cambridge University Press, 2006.
13. D. Cohn, L. Atlas, and R. Ladner. Improving generalization with active learning. Machine Learning, 15(2):201-221, 1994.
14. R. Cohn, L. Atlas, and R. Ladner. Training connectionist networks with queries and selective sampling. In NIPS 2. MIT Press, 1990.
15. S. Dasgupta, D. Hsu, and C. Monteleoni. A general agnostic active learning algorithm. In NIPS 20, pages 353-360. MIT Press, 2008.
16. S. Dasgupta, A. T. Kalai, and C.Monteleoni. Analysis of Perceptron-based active learning. In 18th COLT, pages 249-263. Springer, 2005.
17. Y. Freund, S. Seung, E. Shamir, and N. Tishby. Selective sampling using the query by committee algorithm. Machine Learning, 28(2/3):133-168, 1997. (Pubitemid 127506338)
18. R. Gilad-Bachrach, A. Navot, and N. Tishby. Query by committee made real. NIPS, 18, 2005.
19. T. Joachims. Making large-scale SVM learning practical. In B. Schölkopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods: Support Vector Learning. MIT Press, 1999.
20. C. Monteleoni and M. Kääriäinen. Practical online active learning for classification. In 24th IEEE CVPR, pages 249-263. IEEE Computer Society Press, 2007.
21. J. Shawe-Taylor, C.K.I. Williams, N. Cristianini, and J. Kandola. On the eigenspectrum of the Gram matrix and the generalization error of kernel-PCA. IEEE Trans. IT, 51(7):2510-2522, 2005. (Pubitemid 41136405)
22. I. Steinwart and C. Scovel Fast Rates for Support Vector Machines using Gaussian Kernels Annals of Statistics, 35: 575-607, 2007.
23. S. Tong and D. Koller. Support vector machine active learning with applications to text classification. In 17th ICML, pages 999-1006. Morgan Kaufmann, 2000.
24. A. Tsybakov. Optimal aggregation of classifiers in statistical learning. The Annals of Statistics, 32(1):135-166, 2004. (Pubitemid 41449306)
25. V. Vovk. Competitive on-line statistics. International Statistical Review, 69:213-248, 2001.
26. Y. Ying and D.X. Zhou. Online regularized classification algorithms. IEEE Transactions on Information Theory, 52:4775-4788, 2006. (Pubitemid 44680574)