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The Strength of Weak Learnability (1990) [457 citations — 16 self]

Abstract:

This paper addresses the problem of improving the accuracy of an hypothesis output by a learning algorithm in the distribution-free (PAC) learning modol, A coucept class is learntble (or strongly learnable) if, given access to a source of examples of the unknown concept, the learner with high probability is able to output an hypothesis that is correct on all but aa arbitrarily small h'action of the instances. The concept class is weakly learmtble if tbe learner can preduce an hypothesis that performs only slightly better than madam guessing, In this paper, it is shown that these two notions of learnability are equivalent. A method is described for converting a weak learning algorithm into one flat achieves arbitrarily high accuracy, This construction may have practical applications as a tool for efficiently converting a mediocre learning algorithm into one that per foms extremely well. In addition, the onstroction has some interesting theoretical consequences, including a set of general upper bounds on the complexity of any strong learning algorithm as a function of the allowed error e Keywords. Machine learning, learning from examples, learnability theory, PAC learning, polynomial-time identification, 1.

Citations

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498 Queries and concept learning – Angluin - 1988
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242 Cryptographic limitations on learning boolean formulae and finite automata – Kearns, Valiant - 1994
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177 Finding patterns common to a set of strings – Angluin - 1980
169 Computational limitations on learning from examples – Pitt, Valiant - 1988
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83 Equivalence of models for polynomial learnability – Haussler, Kearns, et al. - 1991
53 Learning decision trees from random examples – Ehrenfeucht, Haussler - 1989
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32 Predicting 0,1-functions on randomly drawn points – Haussler, Littlestone, et al. - 1990
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16 On Space-bounded Learning and the Vapnik-Chervonenkis Dimension – Floyd - 1989
4 Some remarks about space-complexity of learning, and circuit complexity of recognizing – BOUCHERON, SALLANTIN - 1988
4 Expected mistake bounds for on-line learning algorithms. Unpublished manuscript – Haussler, Littlestone, et al. - 1988
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1 Occam's razor, Infotwtation Ptwcessing Letters – Blumer, Ehmnfeucht, et al. - 1987
1 Some remarks about space-complexity of learning, and circuit complexity of recognizing – Bouchemn, Sallantln - 1988
1 Learning decision trees from random exmnples, hfimnatiou and putation – Ehrenfeucht, Haussler - 1989
1 Predicting {0, l}-functions on randomly drawn points – Haussler, Littlestone, et al. - 1988
1 Probability inequalities for sums of bounded randent variables – Hoeffdiug - 1963
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