1. COLT 1988: MIT, MA, USA
David Haussler, Leonard Pitt (Eds.): Proceedings of the First Annual Workshop on Computational Learning Theory, COLT '88, Cambridge, MA, USA, August 3-5, 1988. ACM/MIT 1988- J. Stephen Judd: Learning in Neural Networks. 2-8
- P. Raghavan: Learning in Threshold Networks. 19-27
- Leslie G. Valiant: Functionality in Neural Nets. 28-39
- David Haussler, Michael J. Kearns, Nick Littlestone, Manfred K. Warmuth: 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
- Robert H. Sloan: Types of Noise in Data for Concept Learning. 91-96
- Stéphane Boucheron, Jean Sallantin: 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
- Haim Schweitzer: Non-Learnable Classes of Boolean Formulae That Are Closer Under Variable Permutation. 155-166
- Dana Angluin: Learning With Hints. 167-181
- John Case: The Power of Vacillation. 196-205
- Robert P. Daley: 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
- John C. Cherniavsky, Mahendran Velauthapillai, Richard Statman: Inductive Inference: An Abstract Approach. 251-266
- Ranan B. Banerji: Learning Theories in a Subset of a Polyadic Logic. 267-278
- David Haussler, Nick Littlestone, Manfred K. Warmuth: Predicting {0, 1}-Functions on Randomly Drawn Points. 280-296
- Philip D. Laird: Efficient Unsupervised Learning. 297-311
- Alfredo De Santis, George Markowsky, Mark N. Wegman: Learning Probabilistic Prediction Functions. 312-328
- Yasubumi Sakakibara: Learning Context-Free Grammars from Structural Data in Polynomial Time. 330-344
- Assaf Marron: Learning Pattern Languages from a Single Initial Example and from Queries. 345-358
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