More theorems about scale-sensitive dimensions and learning

Proceedings of the 8th Annual Conference on Computational Learning Theory, COLT 1995

Volumn 1995-January, Issue , 1995, Pages 392-401

  Bartlett, Peter L ^a   Long, Philip M ^b  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^b DUKE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ABSOLUTE ERROR; AGNOSTIC LEARNING; GENERAL UPPER BOUND; GLIVENKO-CANTELLI CLASS; LOWER BOUNDS; PACKING BOUNDS; PREDICTION MODEL; SAMPLE COMPLEXITY;

LEARNING ALGORITHMS;

EID: 85026505196     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/225298.225346     Document Type: Conference Paper

References (16)

1. N. Alon, S. Ben-David, N. Cesa-Bianchi, and D. Haussler. Scale-sensitive dimensions, uniform convergence, and learnability. In Proceedings of the 1993 IEEE Symposium on Foundations of Computer Science. IEEE Press, 1993.
2. M. Anthony and P. Bartlett. Function learning from interpolation. In Computational Learning Theory: EU-ROCOLT'95, 1995.
3. M. Anthony. N. Biggs, and J. Shawe-Taylor. The learnability of formal concepts. In Proceedings of the Third Annual Workshop on Computational Learning Theory, pages 246-257, 1990.
4. P. L. Bartlett, P. M. Long, and R. C. Williamson. Fatshattering and the learnability of real-valued functions. In Proceedings of the Seventh Annual ACM Conference on Computational Learning Theory. ACM Press, New York, 1994.
5. G. M. Benedek and A. Itai. Learnability with respect to fixed distributions. Theoretical Computer Science, 86:377-389, 1991.
6. A. Blumer. A. Ehrenfeucht, D. Haussler, and M. K. Warmuth. Learnability and the Vapnik-Chervonenkis dimension. Journal of the ACM, 36(4):929-965, 1989.
7. A. Ehrenfeucht, D. Haussler, M. Kearns, and L. Valiant. A general lower bound on the number of examples needed for learning. Information and Computation, 82:247-261, 1989.
8. L. Gurvits and P. Koiran. Approximation and learning of convex superpositions. In Computational Learning Theory: EUROCOLT95, 1995.
9. D. Haussler. Decision theoretic generalizations of the PAC model for neural net and other learning applications. Information and Computation, 100:78-150, 1992.
10. D. Haussler. Sphere packing numbers for subsets of the boolean n-cube with bounded Vapnik-Chervonenkis dimension. Journal of Combinatorial Theory, Series A, 69(2):217, 1995.
11. D. Haussler, M. Kearns, N. Littlestone, and M. K. Warmuth. Equivalence of models for polynomial learnability. Inform. Comput., 95(2):129-161, December 1991.
12. D. Haussler, N. Littlestone, and M. K. Warmuth. Predicting {0, 1}-functions on randomly drawn points. Information and Computation, 115(2):129-161, 1994.
13. M. J. Kearns and R. E. Schapire. Efficient distributionfree learning of probabilistic concepts. In Proc. of the 31st Symposium on the Foundations of Comp. Sci, pages 382-391. IEEE Computer Society Press, Los Alamitos, CA, 1990.
14. M. J. Kearns, R. E. Schapire, and L. M. Sellie. Toward efficient agnostic learning. In Proc. 5th Annu. Workshop on Comput. Learning Theory, pages 341-352. ACM Press, New York, NY, 1992.
15. D. Pollard. Convergence of Stochastic Processes. Springer, New York, 1984.
16. H. U. Simon. Bounds on the number of examples needed for learning functions. In Computational Learning Theory: EUROCOLT'93. Oxford University Press, 1994.