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Abstract: A major topic in machine learning is to determine good upper bounds on the true error rates of learned hypotheses based upon their empirical performance on training data. In this paper, we demonstrate new adaptive bounds designed for learning algorithms that operate by making a sequence of choices. These bounds, which we call Microchoice bounds, are similar to Occam-style bounds and can be used to make learning algorithms self-bounding in the style of Freund [Fre98]. We then show how to... (Update)
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BibTeX entry: (Update)
@misc{ langford-microchoice,
author = "Algorithms John Langford",
title = "Microchoice Bounds and Self Bounding Learning",
url = "citeseer.ist.psu.edu/751937.html" }
Citations (may not include all citations):1359 Induction of decision trees (context) - Quinlan - 1986 ACM DBLP
215 Learning decision lists - Rivest - 1987 ACM DBLP
149 Information Processing Letters (context) - Blumer, Ehrenfeucht et al. - 1987
107 Efficient noise-tolerant learning from statistical queries - Kearns - 1993 ACM DBLP
102 An empirical comparison of pruning methods for decision tree.. (context) - Mingers - 1989 ACM DBLP
46 Some PAC-Bayesian theorems - McAllester - 1998 ACM DBLP
26 A framework for structural risk minimization (context) - Shawe-Taylor, Bartlett et al. - 1996
24 Self bounding learning algorithms - Freund - 1998 ACM DBLP
20 A process-oriented heuristic for model selection - Domingos - 1998
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