Abstract is missing.
- Learning in Neural NetworksJ. Stephen Judd. 2-8 [doi]
- Training a 3-Node Neural Network is NP-CompleteAvrim Blum, Ronald L. Rivest. 9-18 [doi]
- Learning in Threshold NetworksP. Raghavan. 19-27 [doi]
- Functionality in Neural NetsLeslie G. Valiant. 28-39 [doi]
- Equivalence of Models for Polynomial LearnabilityDavid Haussler, Michael J. Kearns, Nick Littlestone, Manfred K. Warmuth. 42-55 [doi]
- Results on Learnability and the Vapnick-Chervonenkis DimensionNathan Linial, Yishay Mansour, Ronald L. Rivest. 56-68 [doi]
- Learning Complicated Concepts Reliably and UsefullyRonald L. Rivest, Robert H. Sloan. 69-79 [doi]
- Learnability by Fixed DistributionsGyora M. Benedek, Alon Itai. 80-90 [doi]
- Types of Noise in Data for Concept LearningRobert H. Sloan. 91-96 [doi]
- Learning k-DNF with Noise in the AttributesGeorge Shackelford, Dennis Volper. 97-103 [doi]
- Learning in ParallelJeffrey Scott Vitter, Jyh-Han Lin. 106-124 [doi]
- Some Remarks About Space-Complexity of Learning, and Circuit Complexity of RecognizingStéphane Boucheron, Jean Sallantin. 125-138 [doi]
- A General Lower Bound on the Number of Examples Needed for LearningAndrzej Ehrenfeucht, David Haussler, Michael J. Kearns, Leslie G. Valiant. 139-154 [doi]
- Non-Learnable Classes of Boolean Formulae That Are Closer Under Variable PermutationHaim Schweitzer. 155-166 [doi]
- Learning With HintsDana Angluin. 167-181 [doi]
- Learning Decision Trees from Random ExamplesAndrzej Ehrenfeucht, David Haussler. 182-194 [doi]
- The Power of VacillationJohn Case. 196-205 [doi]
- Prudence in Language LearningStuart A. Kurtz, James S. Royer. 206-219 [doi]
- Transformation of Probabilistic Learning Strategies into Deterministic Learning StrategiesRobert P. Daley. 220-226 [doi]
- Learning via QueriesWilliam I. Gasarch, Carl H. Smith. 227-241 [doi]
- Learning Programs with an Easy to Calculate Set of ErrorsWilliam I. Gasarch, Ramesh K. Sitaraman, Carl H. Smith, Mahendran Velauthapillai. 242-250 [doi]
- Inductive Inference: An Abstract ApproachJohn C. Cherniavsky, Mahendran Velauthapillai, Richard Statman. 251-266 [doi]
- Learning Theories in a Subset of a Polyadic LogicRanan B. Banerji. 267-278 [doi]
- Predicting {0, 1}-Functions on Randomly Drawn PointsDavid Haussler, Nick Littlestone, Manfred K. Warmuth. 280-296 [doi]
- Efficient Unsupervised LearningPhilip D. Laird. 297-311 [doi]
- Learning Probabilistic Prediction FunctionsAlfredo De Santis, George Markowsky, Mark N. Wegman. 312-328 [doi]
- Learning Context-Free Grammars from Structural Data in Polynomial TimeYasubumi Sakakibara. 330-344 [doi]
- Learning Pattern Languages from a Single Initial Example and from QueriesAssaf Marron. 345-358 [doi]
- On the Learnability of Finite AutomataMing Li, Umesh V. Vazirani. 359-370 [doi]
- Learning Regular Languages From CounterexamplesOscar H. Ibarra, Tao Jiang. 371-385 [doi]
- Learning Automata from Ordered ExamplesSara Porat, Jerome A. Feldman. 386-396 [doi]