Leonard Pitt and Leslie Valiant. Computational Limitations on Learning from Examples. Journal of the Association for Computing Machinery, 35:4, pages 965--984, October 1988. Home/Search Document Not in Database Summary ACM TOC Related Articles Check |
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Lower Bounds on Identification Criteria for Perceptron-like.. - Schmitt (1996) (1 citation) (Correct)
....have absolute value at most q(n; n 2; 2) This contradicts the fact that f has weight complexity at ) In the proof we have chosen ffi = 1=2 but any fixed value 0 ffi 1 is sufficient. The method of concentrating a distribution on a set of examples that is hard to be learned goes back to [ Pitt and Valiant, 1988 ] 4 Order identification From the result in the previous section we see that the requirement of producing large weights constitutes a serious obstacle for Perceptron like learning rules not only when exactly but also when PAC identifying Boolean threshold functions. In this section we try to ....
Leonard Pitt and Leslie G. Valiant. Computational limitations on learning from examples. Journal of the Association for Computing Machinery, 35:965--984, 1988.
Learning Algorithms with Applications to Robot Navigation and.. - Singh (1995) (Correct)
....positive or negative examples of the concept. We consider here two standard versions of this model: in one, the learner is required to produce as output a hypothesis belonging to the same class as the target concept, and in the other, the learner s hypotheses may be any polynomial time algorithm [64][50] 66] Several examples are known of concept classes that are hard to learn when hypotheses are restricted to belong to the same class as the target concept but easy to learn when they may belong to a larger class. In particular, Pitt and Valiant [64] showed that learning the class of k term ....
....may be any polynomial time algorithm [64] 50] 66] Several examples are known of concept classes that are hard to learn when hypotheses are restricted to belong to the same class as the target concept but easy to learn when they may belong to a larger class. In particular, Pitt and Valiant [64] showed that learning the class of k term DNF formulas (that is, functions that can be represented by a disjunction of k monomials) is NP hard if the learner is required to produce a k term DNF formula, but is easy if the learner may use a representation of k CNF formulas. In this chapter, we show ....
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Leonard Pitt and Leslie G. Valiant. Computational limitations on learning from examples. Technical report, Harvard University Aiken Computation Laboratory, July 1986.
Recent Results on Boolean Concept Learning - Michael Kearns Ming (1987) (18 citations) Self-citation (Pitt Valiant) (Correct)
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L. Pitt and L.G. Valiant. Computational Limitations on Learning From Examples. Tech. Rept. TR-05-86, Harvard University, 1986, submitted for publication.
A General Lower Bound on the Number - Of Examples Needed Self-citation (Valiant) (Correct)
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Pitt, L., L.G. Valiant, "Computational limitations on learning from examples", technical report TR-05-86, Aiken Computation Laboratory, Harvard University, 1986. 15
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Leonard Pitt and Leslie Valiant. Computational Limitations on Learning from Examples. Journal of the Association for Computing Machinery, 35:4, pages 965--984, October 1988.
Fast Learning of k-Term DNF Formulas with Queries - Avrim Blum Carnegie (1992) (3 citations) (Correct)
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L. Pitt and L. G. Valiant. Computational limitations on learning from examples. Journal of the ACM, 35(4):965--984, 1988.
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Pitt, L., & Valiant, L. G. (1988). Computational limitations on learning from examples. Journal of the ACM, 35, 965-984.
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L Pitt and L G Valiant. Computational limitations on learning from examples. Journal of the ACM, 35(4):965--984, October 1988.
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L. Pitt and L. G. Valiant, "Computational limitations on learning from examples," J. ACM, vol. 35, pp. 965--984, 1988.
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L. Pitt and L. Valiant. Computational limitations on learning from examples. Journal of the ACM, 35:965--984, 1988.
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L. Pitt and L. Valiant. Computational limitations of learning from examples. Journal of the A.C.M., 35(4):965--984, 1988.
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L. Pitt and L.G. Valiant. Computational Limitations on Learning from Examples. Journal of the ACM, 35(4):965-984, 1988.
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L. Pitt and L. G. Valiant, "Computational limitations on learning from examples," J. ACM, vol. 35, pp. 965--984, 1988.
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Leonard Pitt and Leslie G. Valiant. Computational limitations on learning from examples. Journal of the Association for Computing Machinery, 35(4):965--984, October 1988.
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Valiant, L.G., and Pitt, L., "Computational Limitations of Learning from Examples," Journal of the ACM 35, no. 4, pp. 965--984, 1988.
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Lenny Pitt and Leslie G. Valiant. Computational limitations on learning from examples. Journal of the ACM, 35(4): 965--984, October 1988.
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Leonard Pitt and Leslie G. Valiant. Computational limitations on learning from examples. Journal of the Association for Computing Machinery, 35(4):965--984, October 1988.
Cryptography and Machine Learning - Ronald Rivest Laboratory (1993) (1 citation) (Correct)
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Leonard Pitt and Leslie G. Valiant. Computational limitations on learning from examples. Journal of the ACM, 35(4):965-984, 1988.
Efficient Learning of Typical Finite Automata from .. - Freund, Kearns.. (1996) (20 citations) (Correct)
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Leonard Pitt and Leslie G. Valiant. Computational limitations on learning from examples. Journal of the Association for Computing Machinery, 35(4):965--984, October 1988.
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Pitt, L. and Valiant, L. (1988). Computational limitations on learning from examples. J. ACM, 35(4):965-984.
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L. Pitt and L. Valiant. Computational limitations on learning from examples. Journal of the ACM, 35, 1988: 965--984.
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L. Pitt and L. Valiant. Computational limitations on learning from examples. Journal of the ACM, 35, 1988: 965--984.
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Leonard Pitt and Leslie G. Valiant. Computational limitations on learning from examples. Journal of the ACM, 35(4):965--984, 1988.
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