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Semantic Scholar1. COLT 1988: MIT, MA, USA
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FacebookDavid Haussler, Leonard Pitt:
Proceedings of the First Annual Workshop on Computational Learning Theory, COLT '88, Cambridge, MA, USA, August 3-5, 1988. ACM/MIT 1988
Proceedings of the First Annual Workshop on Computational Learning Theory, COLT '88, Cambridge, MA, USA, August 3-5, 1988. ACM/MIT 1988
David Haussler, Michael J. Kearns, Nick Littlestone, Manfred K. Warmuth:
Equivalence of Models for Polynomial Learnability. 42-55
Equivalence of Models for Polynomial Learnability. 42-55
Nathan Linial, Yishay Mansour, Ronald L. Rivest:
Results on Learnability and the Vapnick-Chervonenkis Dimension. 56-68
Results on Learnability and the Vapnick-Chervonenkis Dimension. 56-68
Stéphane Boucheron, Jean Sallantin:
Some Remarks About Space-Complexity of Learning, and Circuit Complexity of Recognizing. 125-138
Some Remarks About Space-Complexity of Learning, and Circuit Complexity of Recognizing. 125-138
Andrzej Ehrenfeucht, David Haussler, Michael J. Kearns, Leslie G. Valiant:
A General Lower Bound on the Number of Examples Needed for Learning. 139-154
A General Lower Bound on the Number of Examples Needed for Learning. 139-154
Haim Schweitzer:
Non-Learnable Classes of Boolean Formulae That Are Closer Under Variable Permutation. 155-166
Non-Learnable Classes of Boolean Formulae That Are Closer Under Variable Permutation. 155-166
Robert P. Daley:
Transformation of Probabilistic Learning Strategies into Deterministic Learning Strategies. 220-226
Transformation of Probabilistic Learning Strategies into Deterministic Learning Strategies. 220-226
William I. Gasarch, Ramesh K. Sitaraman, Carl H. Smith, Mahendran Velauthapillai:
Learning Programs with an Easy to Calculate Set of Errors. 242-250
Learning Programs with an Easy to Calculate Set of Errors. 242-250
John C. Cherniavsky, Mahendran Velauthapillai, Richard Statman:
Inductive Inference: An Abstract Approach. 251-266
Inductive Inference: An Abstract Approach. 251-266
David Haussler, Nick Littlestone, Manfred K. Warmuth:
Predicting {0, 1}-Functions on Randomly Drawn Points. 280-296
Predicting {0, 1}-Functions on Randomly Drawn Points. 280-296
Alfredo De Santis, George Markowsky, Mark N. Wegman:
Learning Probabilistic Prediction Functions. 312-328
Learning Probabilistic Prediction Functions. 312-328
Yasubumi Sakakibara:
Learning Context-Free Grammars from Structural Data in Polynomial Time. 330-344
Learning Context-Free Grammars from Structural Data in Polynomial Time. 330-344
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