On learning visual concepts and DNF formulae (original) (raw)
References
Aizenstein, H. & L. Pitt. (1991). Exact learning of read-twice DNF formulas. In Proceedings of the IEEE Symp. on Foundation of Computer Science, number 32, pages 170–179, San Juan.
Aizenstein, H. & L. Pitt. (1992). Exact learning of read-k disjoint DNF and not-so-disjoint DNF. In Proceedings of COLT '92, pages 71–76.
Angluin, D. (1980). Finding patterns common to a set of strings. Journal of Computer and System Sciences, 21(1):46–62. Google Scholar
Angluin, D., M. Frazier, & L. Pitt (1992). Learning conjunctions of Horn clauses. Machine Learning, 9:147–164. Google Scholar
Angluin, D. & P. Laird. (1988). Learning from noisy examples. Machine Learning, 2(4):343–370. Google Scholar
Aslam, J. A. & S. E. Decatur. (1993). General bounds on statistical query learning and PAC learning with noise via hypothesis boosting. In Proceedings of the 34th Annual Symposium on Foundations of Computer Science, pages 282–291.
Basri, R. (1994). Private Communication.
Bender, M. & D. Roth. (1994). Learning human motion as DNF formulae. (Unpublished).
Blum, A. (1992). Learning boolean functions in an infinite attribute space. Machine Learning, 9(4):373–386. Google Scholar
Blum, A., R. Khardon, A. Kushilevitz, L. Pitt, & D. Roth. (1994). On learning read-k satisfy-j DNF. In Proceedings of the Annual ACM Workshop on Computational Learning Theory, pages 110–117. (Submitted for publication).
Blum, A. & S. Rudich. (1992). Fast learning of _k_-term DNF formulas with queries. In Proceedings of Twenty-Fourth ACM Symposium on Theory of Computing, pages 382–389.
Blumer, A., A. Ehrenfeucht, D. Haussler & M. K. Warmuth. (1987). Occam's razor. Information Processing Letters, 24:377–380. Google Scholar
Bshouty, N. H. (1993). Exact learning via the monotone theory. In Proceedings of the IEEE Symp. on Foundation of Computer Science, pages 302–311, Palo Alto, CA.
Decatur, S. E. (1993). Statistical queries and faulty PAC oracles. In Proceedings of the Sixth Annual ACM Workshop on Computational Learning Theory, pages 262–268.
Hancock, T. (1991). Learning 2μ DNF formulas and _k_μ decision trees. In Proceedings of the Fourth Annual Workshop on Computational Learning Theory, pages 199–209.
Haussler, D., M. Kearns, N. Littlestone & M. K. Warmuth. (1991). Equivalence of models for polynomial learnability. Information and Computation, 95(2):129–161. Google Scholar
Jackson, J. (1994). An efficient membership-query algorithm for learning DNF with respect to the uniform distribution. In Proceedings of the IEEE Symp. on Foundation of Computer Science. To Appear.
Jerrum, M. (1991). Simple translation-invariant concepts are hard to learn. Technical Report CSR-12-91, University of Edinburgh, Department of Computer Science.
Kearns, M. (1993). Efficient noise-tolerant learning from statistical queries. In Proceedings of the Twenty-Fifth Annual ACM Symposium on Theory of Computing, pages 392–401.
Kearns, M. & M. Li. (1993). Learning in the precence of malicious error. Siam Journal of Computing, 22(4).
Kearns, M., M. Li, L. Pitt, & L. G. Valiant. (1987). On the learnability of boolean formulae. In Proceedings of the Nineteenth Annual ACM Symposium on Theory of Computing, pages 285–295.
Kearns, M. & L. Pitt. (1989). A polynomial-time algorithm for learning _k_-variable pattern languages from examples. In Proceedings of the Second Annual Workshop on Computational Learning Theory, pages 57–71.
Kushilevitz, E. & Y. Mansour. (1993). Learning decision trees using the fourier spectrum. Siam Journal of Computing, 22(6):1331–1348. Earlier version appeared in Proc. 23rd Ann. IEEE Symp. on Foundations of Computer Science, 1991. Google Scholar
Li, M. & P. M. B. Vitanyi. (1989). A theory of learning simple concepts under simple distributions and average case complexity for the universal distribution. In Proceedings of the Thirtieth Annual Symposium on Foundations of Computer Science, pages 34–39.
Littlestone, N. (1988). Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm. Machine Learning, 2:285–318. Google Scholar
Littlestone, N. (1989). Mistake bounds and logarithmic linear-threshold learning algorithms. Ph.D. thesis, U. C. Santa Cruz.
Mitchell, T.M., R.M. Keller, & S.T. Kedar-Cabelli. (1986). Explanation Based Learning. Machine Learning, 1(1):47–80. Google Scholar
Pillapakamnatt, K. & V. Raghavan. (1993). Read twice DNF formulas are properly learnable. Technical Report TR-CS-93-59, Vanderbilt University, Computer Science Department. To appear, Proceedings of the 1st European Conference on Computational Learning Theory (EuroColt 93).
Rivest, R. L. (1987). Learning decision lists. Machine Learning, 2(3):229–246. Google Scholar
Schapire, R. E. (1990). Pattern languages are not learnable. In Proceedings of COLT '90, pages 122–129.
Shackelford, G. & D. Volper. (1988). Learning k-DNF with noise in the attributes. In First Workshop on Computatinal Learning Theory, pages 97–103.
Shvaytser, H. (1990). Learnable and nonlearnable visual concepts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(5):459–466. Google Scholar
Valiant, L. G. (1984). A theory of the learnable. Communications of the ACM, 27(11):1134–1142. Google Scholar
Valiant, L. G. (1985). Learning disjunctions of conjunctions. In Proceedings of the International Joint Conference of Artificial Intelligence, pages 560–566.