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ORCID1st COLT 1988: MIT, MA, USA
- David 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 - J. Stephen Judd:
Learning in Neural Networks. 2-8 - Avrim Blum, Ronald L. Rivest:
Training a 3-Node Neural Network is NP-Complete. 9-18 - 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 - Ronald L. Rivest, Robert H. Sloan:
Learning Complicated Concepts Reliably and Usefully. 69-79 - Gyora M. Benedek, Alon Itai:
Learnability by Fixed Distributions. 80-90 - Robert H. Sloan:
Types of Noise in Data for Concept Learning. 91-96 - George Shackelford, Dennis Volper:
Learning k-DNF with Noise in the Attributes. 97-103 - Jeffrey Scott Vitter, Jyh-Han Lin:
Learning in Parallel. 106-124 - 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 - Andrzej Ehrenfeucht, David Haussler:
Learning Decision Trees from Random Examples. 182-194 - John Case:
The Power of Vacillation. 196-205 - Stuart A. Kurtz, James S. Royer:
Prudence in Language Learning. 206-219 - Robert P. Daley:
Transformation of Probabilistic Learning Strategies into Deterministic Learning Strategies. 220-226 - William I. Gasarch, Carl H. Smith:
Learning via Queries. 227-241 - 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 - Ming Li, Umesh V. Vazirani:
On the Learnability of Finite Automata. 359-370 - Oscar H. Ibarra, Tao Jiang:
Learning Regular Languages From Counterexamples. 371-385 - Sara Porat, Jerome A. Feldman:
Learning Automata from Ordered Examples. 386-396
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