Linear concepts and hidden variables

Machine Learning

Volumn 42, Issue 1-2, 2001, Pages 123-141

  Grove, Adam J ^a,c   Roth, Dan ^b  

^a NEC LABORATORIES AMERICA   (United States)

^b University of Illinois at Urbana Champaign   (United States)

^c NONE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; FUNCTIONS; MATHEMATICAL MODELS; PROBABILITY;

COVARIANCES; HIDDEN VARIABLES; LINEAR FUNCTIONS;

LEARNING SYSTEMS;

EID: 0034817644     PISSN: 08856125     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1007655119445     Document Type: Article

References (15)

1. Blum, A. (1997). Empirical support for winnow and weighted majority based algorithms: results on a calendar scheduling domain. Machine Learning, 26, 1-19.
2. Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.
3. Cryan, M., Goldberg, L. A., & Goldberg, P. W. (1998). Evolutionary trees can be learned in polynomial time in the two-state general markov model. In 39th Symp. on Foundations of Computer Science (pp. 436-445).
4. Cortes, C. & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273-297.
5. Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Royal Statistical Society B, 39, 1-38.
6. Freund, Y. & Mansour, Y. (1999). Estimating the mixture of two product distributions. In Proc. 12th Annu. Conf. on Comput. Learning Theory (pp. 53-62). New York, NY: ACM Press.
7. Golding, A. R. & Roth, D. (1999). A Winnow based approach to context-sensitive spelling correction. Machine Learning, 34(1-3), 107-130.
8. Hogg, R. V. & Craig, A. T. (1995). Introduction to mathematical statistics. Englewood Cliffs, NJ: Prentice-Hall.
9. Höffgen, K. & Simon, H. (1992). Robust trainability of single neurons. In Proc. 5th Annu. Workshop on Comput. Learning Theory (pp. 428-439). New York, NY: ACM Press.
10. Kearns, M. & Schapire, R. (1994). Efficient distribution-free learning of probabilistic concepts. Journal of Computer and System Sciences, 48, 464-497.
11. Littlestone, N. (1988). Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm. Machine Learning, 2, 285-318.
12. Littlestone, N. (1991). Redundant noisy attributes, attribute errors, and linear threshold learning using Winnow. In Proc. 4th Annu. Workshop on Comput. Learning Theory (pp. 147-156). San Mateo, CA: Morgan Kaufmann.
13. Minsky, M. L. & Papert, S. A. (1969). Perceptrons. Cambridge, MA: MIT Press.
14. Valiant, L. G. (1984). A theory of the learnable. Communications of the ACM, 27(11), 1134-1142.
15. Vapnik, V. N. (1995). The nature of statistical learning theory. New York: Springer-Verlag.