Abstract is missing.
- Learning Boolean Read-Once Formulas with Arbitrary Symmetric and Constant Fan-in GatesNader H. Bshouty, Thomas R. Hancock, Lisa Hellerstein. 1-15 [doi]
- On-line Learning of RectanglesZhixiang Chen, Wolfgang Maass. 16-28 [doi]
- Cryptographic Lower Bounds for Learnability of Boolean Functions on the Uniform DistributionMichael Kharitonov. 29-36 [doi]
- Learning Hierarchical Rule SetsJyrki Kivinen, Heikki Mannila, Esko Ukkonen. 37-44 [doi]
- Random DFA s Can Be Approximately Learned from Sparse Uniform ExamplesKevin J. Lang. 45-52 [doi]
- An O(n:::::::log log n:::::::) Learning Algorithm for DNF Under the Uniform DistributionYishay Mansour. 53-61 [doi]
- A Technique for Upper Bounding the Spectral Norm with Applications to LearningMihir Bellare. 62-70 [doi]
- Exact Learning of Read-::::k:::: Disjoint DNF and Not-So-Disjoint DNFHoward Aizenstein, Leonard Pitt. 71-76 [doi]
- Learning ::::k::::-Term DNF Formulas with an Incomplete Membership OracleSally A. Goldman, H. David Mathias. 77-84 [doi]
- Learning DNF Formulae Under Classes of Probability DistributionsMichele Flammini, Alberto Marchetti-Spaccamela, Ludek Kucera. 85-92 [doi]
- A Theory for Memory-Based LearningJyh-Han Lin, Jeffrey Scott Vitter. 103-115 [doi]
- Learnability of Description LogicsWilliam W. Cohen, Haym Hirsh. 116-127 [doi]
- PAC-Learnability of Determinate Logic ProgramsSaso Dzeroski, Stephen Muggleton, Stuart J. Russell. 128-135 [doi]
- Polynomial Time Inference of a Subclass of Context-Free TransformationsHiroki Arimura, Hiroki Ishizaka, Takeshi Shinohara. 136-143 [doi]
- A Training Algorithm for Optimal Margin ClassifiersBernhard E. Boser, Isabelle Guyon, Vladimir Vapnik. 144-152 [doi]
- The Learning Complexity of Smooth Functions of a Single VariableDon Kimber, Philip M. Long. 153-159 [doi]
- Absolute Error Bounds for Learning Linear Functions OnlineEthan Bernstein. 160-163 [doi]
- Probably Almost Discriminative LearningKenji Yamanishi. 164-171 [doi]
- PAC Learning With Generalized Samples and an Application to Stochastic GeometrySanjeev R. Kulkarni, John N. Tsitsiklis, Sanjoy K. Mitter, Ofer Zeitouni. 172-179 [doi]
- Degrees of InferabilityPeter Cholak, Efim B. Kinber, Rodney G. Downey, Martin Kummer, Lance Fortnow, Stuart A. Kurtz, William I. Gasarch, Theodore A. Slaman. 180-192 [doi]
- On Learning Limiting ProgramsJohn Case, Sanjay Jain, Arun Sharma. 193-202 [doi]
- Breaking the Probability ::::1/2:::: Barrier in FIN-Type LearningRobert P. Daley, Bala Kalyanasundaram, Mahendran Velauthapillai. 203-217 [doi]
- Case-Based Learning in Inductive InferenceKlaus P. Jantke. 218-223 [doi]
- Generalization versus ClassificationRolf Wiehagen, Carl H. Smith. 224-230 [doi]
- Learning Switching ConceptsAvrim Blum, Prasad Chalasani. 231-242 [doi]
- Learning With a Slowly Changing DistributionPeter L. Bartlett. 243-252 [doi]
- Dominating Distributions and LearnabilityGyora M. Benedek, Alon Itai. 253-264 [doi]
- Learning Stochastic Functions by Smooth Simultaneous EstimationKevin Buescher, P. R. Kumar. 272-279 [doi]
- On Learning Noisy Threshold Functions with Finite Precision WeightsRonny Meir, José F. Fontanari. 280-286 [doi]
- Query by CommitteeH. Sebastian Seung, Manfred Opper, Haim Sompolinsky. 287-294 [doi]
- A Noise Model on Learning Sets of StringsYasubumi Sakakibara, Rani Siromoney. 295-302 [doi]
- Language Learning from Stochastic InputShyam Kapur, Gianfranco Bilardi. 303-310 [doi]
- On Exact Specification by ExamplesMartin Anthony, Graham Brightwell, David A. Cohen, John Shawe-Taylor. 311-318 [doi]
- A Computational Model of TeachingJeffrey Jackson, Andrew Tomkins. 319-326 [doi]
- Approximate Testing and LearnabilityKathleen Romanik. 327-332 [doi]
- Characterizations of Learnability for Classes of {::::O, ..., n::::}-Valued FunctionsShai Ben-David, Nicolò Cesa-Bianchi, Philip M. Long. 333-340 [doi]
- Toward Efficient Agnostic LearningMichael J. Kearns, Robert E. Schapire, Linda Sellie. 341-352 [doi]
- PAB-Decisions for Boolean and Real-Valued FeaturesSvetlana Anoulova, Paul Fischer, Stefan Pölt, Hans-Ulrich Simon. 353-362 [doi]
- On the Role of Procrastination for Machine LearningRusins Freivalds, Carl H. Smith. 363-376 [doi]
- Types of Monotonic Language Learning and Their CharacterizationSteffen Lange, Thomas Zeugmann. 377-390 [doi]
- An Improved Boosting Algorithm and Its Implications on Learning ComplexityYoav Freund. 391-398 [doi]
- Some Weak Learning ResultsDavid P. Helmbold, Manfred K. Warmuth. 399-412 [doi]
- Robust Trainability of Single NeuronsKlaus-Uwe Höffgen, Hans-Ulrich Simon. 428-439 [doi]
- On the Computational Power of Neural NetsHava T. Siegelmann, Eduardo D. Sontag. 440-449 [doi]
- Corrigendum to Types of Noise in Data for Concept LearningRobert H. Sloan. 450 [doi]