Sharpening Occam's razor

被引:8
|
作者
Li, M
Tromp, J
Vitányi, P
机构
[1] Ctr Wiskunde & Informat, NL-1098 SJ Amsterdam, Netherlands
[2] Univ Amsterdam, NL-1012 WX Amsterdam, Netherlands
[3] Univ Calif Santa Barbara, Dept Comp Sci, Santa Barbara, CA 93106 USA
关键词
analysis of algorithms; Pac-learning; Kolmogorov complexity; Occam's razor-style theorems;
D O I
10.1016/S0020-0190(02)00427-1
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We provide a new representation-independent formulation of Occam's razor theorem, based on Kolmogorov complexity. This new formulation allows us to: (i) obtain better sample complexity than both length-based [Blumer et al., Inform. Process. Lett. 24 (1987) 377-380] and VC-based [Blumer et al., J. ACM 35 (1989) 929-965] versions of Occam's razor theorem, in many applications; and (ii) achieve a sharper reverse of Occam's razor theorem than that of Board and Pitt [STOC, 1999, pp. 54-63]. Specifically, we weaken the assumptions made by Board and Pitt [STOC, 1999, pp. 54-63] and extend the reverse to superpolynomial running times. (C) 2002 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:267 / 274
页数:8
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