Maximizing agreements with one-sided error with applications to heuristic learning

Machine Learning

Volumn 59, Issue 1-2, 2005, Pages 99-123

  Bshouty, Nader H ^a   Burroughs, Lynn ^b  

^a TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

^b UNIVERSITY OF CALGARY   (Canada)

Author keywords

Agnostic learning; Approximation; Boolean formulas; Example based learning; Heuristic learning; Maximizing agreements

Indexed keywords

APPROXIMATION THEORY; BOOLEAN FUNCTIONS; CONSTRAINT THEORY; ERRORS; HEURISTIC METHODS; PROBLEM SOLVING; RANDOM PROCESSES;

AGNOSTIC LEARNING; BOOLEAN FORMULAS; EXAMPLE-BASED LEARNING; HEURISTIC LEARNING; MAXIMIZING AGREEMENTS;

LEARNING SYSTEMS;

EID: 21244495658     PISSN: 08856125     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10994-005-0464-5     Document Type: Article

References (20)

1. Amaldi, E., & Kann, V. (1995). 'The complexity and approximahility of finding maximum feasible subsystems of linear relations. Theoretical Computer Science, 147:1/2, 181-210.
2. Angluin, D., & Laird, P. D. (1987). Learning from noisy examples. Machine learning, 2:4, 343-370.
3. Bartleu, P. L., & Hen-David, S. (1999). Hardness results for neural network approximation problems. In Proceedings of the 4th Kuropean Conference on Computational Learning Theory (pp. 50-62).
4. Den-David, S., Eiron, N., & Long, P. M. (2003). On the difficulty of approximately maximizing agreements. Journal of Computer and System Sciences, 66:3, 496-514.
5. Blum, A., Furst, M., Jackson, J., Kearns, M., Mansour, Y., & Rudich, S. (1994). Weakly learning DM- and characterizing statistical query learning using Fourier analysis. In Proceedings of the 26th Annual ACM Symposium on Theory of Computing (pp. 253-262).
6. Blum, A. L., & Rivest, R. L. (1988). Training a 3-node neural network is NP-complete. In Proceedings of the 1988 Workshop on Computational learning Theory (pp. 9-18).
7. Blumer, A., Ehrenfeucht, A., Haussler, D., & Warmuth, M. K. (1987). Occam's razor. Information Processing Letters, 24:6, 377-380.
8. Bshouty, N. H., & Burroughs, L. (2002a). Bounds for the minimum disagreement problem with applications to learning theory. In Proceedings of the 15th Annual Conference on Computational learning Theory (pp. 271-286).
9. Bshouty, N. H., & Burroughs, L. (2002b). Maximizing agreements and coagnostic learning. In Proceedings of the 13th International Conference on Algorithmic Learning Theory.
10. 1'. In Proceedings of the 37th Annual IEEE Symposium on Foundations of Computer Science (pp. 627-636).
11. Håstad, J. (1997). Some optimal inapproximability results. In Proceedings of the 29th Annual ACM Symposium on Theory of Computing (pp. 1-10).
12. Höffgen, K.-U., Simon, H.-U;., & Van Horn, K. S. (1995). Robust trainability of single neurons, JCSS, 50:1, 114-125.
13. Itoh, T. (2000). Approximating the maximum weight of linear codes is APX-complete. On Fundamentals of Electronics, Communications and Computer Sciences, E83-A:4, 606-613.
14. Kann, V., Khanna, S., Lagergren, J., & Panconesi, A. (1996). On the hardness of approximating max-k-cut and its dual. In Proceedings of the Fourth Israeli Symposium on Theory of Computing and Systems (pp. 61-67).
15. Kearns, M., & Li, M. (1993). learning in the presence of malicious errors. SIAM Journal on Computing, 22:4, 807-837.
16. Kuhlmann, C. (2000). Hardness results for general two-layer neural networks. In Proceedings of the 13th Annual Conference on Computational Learning Theory (pp. 275-285).
17. Panconesi, A., & Ranjan, D. (1993). Quantifiers and approximation. Theoretical Computer Science, 107:1, 145-163.
18. Papadimitriou, C., & Yannakakis, M. (1991). Optimization, approximation and complexity classes. Journal of Computer and System Sciences, 43, 425-440.
19. Pitt, L., & Valiant, L. G. (1988). Computational limitations on learning from examples. JACM, 35:4, 965-984.
20. Valiant, L. G. (1984). A theory of the learnable. Communications of the ACM, 27:11, 1134-1142.