On Using Extended Statistical Queries to Avoid Membership Queries

Journal of Machine Learning Research

Volumn 2, Issue 3, 2002, Pages 359-395

  Bshouty, Nader H ^a   Feldman, Vitaly ^a  

^a TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0037708179     PISSN: 15324435     EISSN: None     Source Type: Journal    
DOI: 10.1162/153244302760200669     Document Type: Article

References (30)

1. [AD93] Javed Aslam, Scott Decatur. General Bounds on Statistical Query Learning and PAC Learning with Noise via Hypothesis Boosting. In Proceedings of 34-th Annual Symposium on Foundations of Computer Science, pp. 282-291, 1993.
2. [AL88] Dana Angluin, Philip Laird. Learning Prom Noisy Examples. In Machine Learning, 2(4) pp.343-370, 1988.
3. [AK91] Dana Angluin, Michael Kharitonov. When Won't Membership Queries Help? In Proceedings of the 23-rd Annual ACM Symposium on Theory of Computing, 1991, pp. 454-454.
4. [Bs93] Nader Bshouty. Exact Learning via the Monotone Theory. In Proceedings of 34-th Annual Symposium on Foundations of Computer Science, pp. 302-311, 1993.
5. +94] Avrim Blum, Merrick Furst, Jeffrey Jackson, Michael Kearns, Yishay Mansour, Steven Rudich. Weakly Learning DNF and Characterizing Statistical Query Learning Using Fourier Analysis. In Proceedings of the 26-th Annual ACM Symposium on the Theory of Computing, pp. 253-262, 1994.
6. [BF01] Nader Bshouty, Vitaly Feldman. On Using Extended Statistical Queries to Avoid Membership Queries. In Proceedings of the 14-th Annual Conference on COLT/EuroCOLT, pp. 529-545, 2001.
7. [BG01] Nader Bshouty, Dmitry Gavinsky. On Boosting With Optimal Poly-Bounded Distributions. In Proceedings of the 14-th Annual Conference on COLT/EuroCOLT, pp. 490-506, 2001.
8. [BJT99] Nader Bshouty, Jeffrey Jackson, Christino Tamon. Uniform-Distribution Attribute Noise Learnability. In Proceedings of the 12-th Annual Conference on COLT, pp. 75-80, 1999.
9. [BKW00] Avrim Blum, Adam Kalai, Hal Wasserman. Noise-Tolerant Learning, the Parity Problem and the Statistical Query Model. In Proceedings of the 32-th Annual ACM Symposium on Theory of Computing, pp. 435-440, 2000.
10. [DG95] Scott Decatur, Rosario Gennaro. In Proceedings of the Eighth Annual ACM Workshop on Computational Learning Theory, pp.353-360, 1995.
11. [Fr90] Yoav Freund. Boosting a Weak Learning Algorithm by Majority. In Proceedings of the Third Annual Workshop on COLT, pp. 202-216, 1990.
12. [FS94] Yoav Freund, Robert Schapire. A Decision-theoretic Generalization of On-line Learning and an Application to Boosting. In Proceedings of the Second Annual European Conference on Computational Learning Theory, 1994.
13. [GKS94] Sally Goldman, Michael Kearns, Robert Shapire. Exact Identification of Circuits Using Fixed Points of Amplification Functions, In SIAM Journal on Computing, 22 (1993), pp. 705-726.
14. [GL89] Oded Goldreich, Leonid A. Levin. A Hard-Core Predicate for all One-Way Functions. In Proceedings of the Twenty First Annual ACM Symposium on Theory of Computing, pp. 25-32, 1989.
15. [GS95] Sally Goldman, Robert Sloan. Can PAC Learning Algorithms Tolerate Random Attribute Noise? In Algorithmica, 14(1) pp. 70-84, 1995.
16. [Ja94] Jeffrey Jackson. An Efficient Membership-Query Algorithm for Learning DNF with Respect to the Uniform Distribution. In Proceedings of the 35th Annual Symposion on Foundations of Computer Science, pp. 42-53, 1994.
17. [Ja97] Jeffrey Jackson. An Efficient Membership-Query Algorithm for Learning DNF with Respect to the Uniform Distribution. In Journal of Computer and System Sciences, 55(3), 1997
18. [JSS97] Jeffrey Jackson, Eli Shamir, Clara Shwartzman. Learning with Queries Corrupted by Classification Noise. In Proceedings of Fifth Israel Symposium on Theory of Computing and Systems, pp. 45-53, 1997.
19. [Kea93] Michael Kearns. Efficient Noise-Tolerant Learning from Statistical Queries. In Proceedings of the Forth Annual Workshop on COLT, pp. 392-401, 1993.
20. [KM91] Eyal Kushilevitz, Yishay Mansour. Learning Decision Trees Using the Fourier Spectrum. In Proceedings of the 23-rd Annual Symposium on Theory of Computing, pages 455-464.
21. [KS99] Adam Klivans, Rocco Servedio. Boosting and Hard-Core Sets. In Proceedings of 40-th Annual Symposium on Foundations of Computer Science, pp. 624-633, 1999.
22. [KSS92] Michael Kearns, Robert Shapire, Linda Sellie. Toward Efficient Agnostic Learning. In Proceedings of the Fifth Annual Workshop on COLT, pp. 341-352, 1992.
23. [Lai88] Phillip D. Laird. Learning From Good and Bad Data. Kluwer Academic Publishers, Boston, 1988.
24. [LMN89] Nathan Linial, Yishay Mansour, Noam Nisan. Constant Depth Circuits, Fourier Transform, and Learnability. In Proceedings of the 31-st Symposium on the Foundations of Computer Science, pp. 574-579, 1989.
25. [Man94] Yishay Mansour. Learning Boolean Functions via the Fourier Transform. In Theoretical Advances in Neural Computation and Learning, (V.P. Roychodhury and K-Y. Siu and A. Orlitsky, ed.), 391-424, 1994.
26. [ShSh95] Eli Shamir, Clara Shwartzman. Learning by Extended Statistical Queries and Its Relation to PAC Learning. In Proceedings of Second European Conference, EuroCOLT '95, pp. 357-366, 1995.
27. [Sch90] Robert Shapire. The Strength of Weak Learnability. In Machine Learning, 5, pp. 197-227, 1990.
28. [S188] Robert Sloan. Types of Noise in Data for Concept Learning. In Proceedings of the 1988 Workshop on Computational Learning Theory, pp. 91-96, MIT, ACM Press, 1988.
29. [SV88] George Shakelford, Dennis Volper. Learning k-DNF with Noise in the Attributes. In Proceedings of the 1988 Workshop on COLT, pp. 97-103, 1988.
30. [V84] Leslie Valiant. A Theory of the Learnable. In Commmunications of the ACM, 27(11) pp. 1134-1142, 1984.