When can two unsupervised learners achieve PAC separation?

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2111, Issue , 2001, Pages 303-319

  Goldberg, Paul W ^a  

^a UNIVERSITY OF WARWICK   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

PROBABILITY DISTRIBUTIONS; SEPARATION; UNSUPERVISED LEARNING;

BOOLEAN DOMAIN; CONCEPT CLASS; DECISION BOUNDARY; GEOMETRIC CONCEPTS; LEARNING SETTINGS; PAC LEARNING; PARITY FUNCTIONS; PRODUCT DISTRIBUTIONS;

LEARNING ALGORITHMS;

EID: 33646502978     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44581-1_20     Document Type: Conference Paper

References (21)

1. E.L. Allwein, R.E. Schapire and Y. Singer (2000). Reducing Multiclass to Binary: A Unifying Approachfor Margin Classifiers. Journal of Machine Learning Research 1, 113-141.
2. M. Anthony and N. Biggs (1992). Computational Learning Theory, Cambridge Tracts in Theoretical Computer Science, Cambridge University Press, Cambridge.
3. C.M. Bishop (1995). Neural Networks for Pattern Recognition, Oxford University Press.
4. A. Blumer, A. Ehrenfeucht, D. Haussler and M.K. Warmuth (1989). Learnability and the Vapnik-Chervonenkis Dimension, J.ACM 36, 929-965.
5. N.H. Bshouty, S.A. Goldman, H.D. Mathias, S. Suri and H. Tamaki (1998). Noise- Tolerant Distribution-Free Learning of General Geometric Concepts. Journal of the ACM 45(5), pp. 863-890.
6. N.H. Bshouty, P.W. Goldberg, S.A. Goldman and H.D. Mathias (1999). Exact learning of discretized geometric concepts. SIAM J. Comput. 28(2) pp. 674-699.
7. N. Cristianini and J. Shawe-Taylor (2000). An Introduction to Support Vector Machines. Cambridge University Press.
8. M. Cryan, L. Goldberg and P. Goldberg (1998). Evolutionary Trees can be Learned in Polynomial Time in the Two-State General Markov Model. Procs. of 39th FOCS symposium, pp. 436-445.
9. S. Dasgupta (1999). Learning mixtures of Gaussians. 40th IEEE Symposium on Foundations of Computer Science.
10. R.O. Duda and P.E. Hart. Pattern Classification and Scene Analysis. Wiley, New York (1973).
11. Y. Freund and Y. Mansour (1999). Estimating a mixture of two product distributions. Procs. of 12th COLT conference, pp. 53-62.
12. A. Frieze, M. Jerrum and R. Kannan (1996). Learning Linear Transformations. 37th IEEE Symposium on Foundations of Computer Science, pp. 359-368.
13. V. Guruswami and A. Sahai (1999). Multiclass Learning, Boosting, and Error- Correcting Codes. Procs. of 12th COLT conference, pp. 145-155.
14. D. Haussler, M. Kearns, N. Littlestone and M.K. Warmuth (1991). Equivalence of Models for Polynomial Learnability. Information and Computation, 95(2), pp. 129-161.
15. D. Helmbold, R. Sloan and M.K. Warmuth (1992). Learning Integer Lattices. SIAM Journal on Computing, 21(2), pp. 240-266.
16. M.J. Kearns (1993). Efficient Noise-Tolerant Learning From Statistical Queries, Procs. of the 25th Annual Symposium on the Theory of Computing, pp. 392-401.
17. M. Kearns, Y. Mansour, D. Ron, R. Rubinfeld, R.E. Schapire and L. Sellie (1994). On the Learnability of Discrete Distributions, Proceedings of the 26th Annual ACM Symposium on the Theory of Computing, pp. 273-282.
18. M.J. Kearns and R.E. Schapire (1994). Efficient Distribution-free Learning of Probabilistic Concepts, Journal of Computer and System Sciences, 48(3) 464-497. (see also FOCS’90)
19. J.C. Platt, N. Cristianini and J. Shawe-Taylor (2000). Large Margin DAGs for Multiclass Classification, Procs. of 12th NIPS conference.
20. L.G. Valiant (1984). A Theory of the Learnable. Commun. ACM 27(11), pp. 1134-1142.
21. L.G. Valiant (1985). Learning disjunctions of conjunctions. Procs. of 9th International Joint Conference on Artificial Intelligence.