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
- 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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