Sharpening Occam's razor
暂无分享,去创建一个
Ming Li | Paul M. B. Vitányi | John Tromp | P. Vitányi | Ming Li | J. Tromp
[1] Robert E. Schapire,et al. The Boosting Approach to Machine Learning An Overview , 2003 .
[2] David Haussler,et al. Predicting {0,1}-functions on randomly drawn points , 1988, COLT '88.
[3] Temple F. Smith. Occam's razor , 1980, Nature.
[4] Manfred K. Warmuth. Towards Representation Independence in PAC Learning , 1989, AII.
[5] William I. Gasarch,et al. Book Review: An introduction to Kolmogorov Complexity and its Applications Second Edition, 1997 by Ming Li and Paul Vitanyi (Springer (Graduate Text Series)) , 1997, SIGACT News.
[6] Leonard Pitt,et al. On the necessity of Occam algorithms , 1990, STOC '90.
[7] Leslie G. Valiant,et al. A theory of the learnable , 1984, STOC '84.
[8] Ming Li,et al. An Introduction to Kolmogorov Complexity and Its Applications , 2019, Texts in Computer Science.
[9] David Haussler,et al. Quantifying Inductive Bias: AI Learning Algorithms and Valiant's Learning Framework , 1988, Artif. Intell..
[10] Manfred K. Warmuth,et al. On Weak Learning , 1995, J. Comput. Syst. Sci..
[11] Tao Jiang,et al. Linear approximation of shortest superstrings , 1994, JACM.
[12] Ming Li,et al. Towards a DNA sequencing theory (learning a string) , 1990, Proceedings [1990] 31st Annual Symposium on Foundations of Computer Science.
[13] David Haussler,et al. Learnability and the Vapnik-Chervonenkis dimension , 1989, JACM.
[14] Martin Anthony,et al. Computational learning theory: an introduction , 1992 .
[15] Leslie G. Valiant,et al. A general lower bound on the number of examples needed for learning , 1988, COLT '88.