5. COLT 1992: Pittsburgh, PA, USA
David Haussler (Ed.): Proceedings of the Fifth Annual ACM Conference on Computational Learning Theory, COLT 1992, Pittsburgh, PA, USA, July 27-29, 1992. ACM 1992 ISBN 0-89791-497-X- Nader H. Bshouty, Thomas R. Hancock, Lisa Hellerstein: Learning Boolean Read-Once Formulas with Arbitrary Symmetric and Constant Fan-in Gates. 1-15
- Michael Kharitonov: Cryptographic Lower Bounds for Learnability of Boolean Functions on the Uniform Distribution. 29-36
- Kevin J. Lang: Random DFA's Can Be Approximately Learned from Sparse Uniform Examples. 45-52
- Yishay Mansour: An O(nlog log n) Learning Algorithm for DNF Under the Uniform Distribution. 53-61
- Mihir Bellare: A Technique for Upper Bounding the Spectral Norm with Applications to Learning. 62-70
- Howard Aizenstein, Leonard Pitt: Exact Learning of Read-k Disjoint DNF and Not-So-Disjoint DNF. 71-76
- Sally A. Goldman, H. David Mathias: Learning k-Term DNF Formulas with an Incomplete Membership Oracle. 77-84
- Michele Flammini, Alberto Marchetti-Spaccamela, Ludek Kucera: Learning DNF Formulae Under Classes of Probability Distributions. 85-92
- Santosh S. Venkatesh, Robert R. Snapp, Demetri Psaltis: Bellman Strikes Again! The Growth Rate of Sample Complexity with Dimension for the Nearest Neighbor Classifier. 93-102
- Saso Dzeroski, Stephen Muggleton, Stuart J. Russell: PAC-Learnability of Determinate Logic Programs. 128-135
- Hiroki Arimura, Hiroki Ishizaka, Takeshi Shinohara: Polynomial Time Inference of a Subclass of Context-Free Transformations. 136-143
- Bernhard E. Boser, Isabelle Guyon, Vladimir Vapnik: A Training Algorithm for Optimal Margin Classifiers. 144-152
- Don Kimber, Philip M. Long: The Learning Complexity of Smooth Functions of a Single Variable. 153-159
- Ethan Bernstein: Absolute Error Bounds for Learning Linear Functions Online. 160-163
- Kenji Yamanishi: Probably Almost Discriminative Learning. 164-171
- Sanjeev R. Kulkarni, John N. Tsitsiklis, Sanjoy K. Mitter, Ofer Zeitouni: PAC Learning With Generalized Samples and an Application to Stochastic Geometry. 172-179
- Peter Cholak, Efim B. Kinber, Rodney G. Downey, Martin Kummer, Lance Fortnow, Stuart A. Kurtz, William I. Gasarch, Theodore A. Slaman: Degrees of Inferability. 180-192
- Robert P. Daley, Bala Kalyanasundaram, Mahendran Velauthapillai: Breaking the Probability 1/2 Barrier in FIN-Type Learning. 203-217
- Klaus P. Jantke: Case-Based Learning in Inductive Inference. 218-223
- Peter L. Bartlett: Learning With a Slowly Changing Distribution. 243-252
- Alberto Bertoni, Paola Campadelli, Anna Morpurgo, Sandra Panizza: Polynomial Iniform Convergence and Polynomial-Sample Learnability. 265-271
- Kevin Buescher, P. R. Kumar: Learning Stochastic Functions by Smooth Simultaneous Estimation. 272-279
- Ronny Meir, José F. Fontanari: On Learning Noisy Threshold Functions with Finite Precision Weights. 280-286
- Martin Anthony, Graham Brightwell, David A. Cohen, John Shawe-Taylor: On Exact Specification by Examples. 311-318
- Kathleen Romanik: Approximate Testing and Learnability. 327-332
- Shai Ben-David, Nicolò Cesa-Bianchi, Philip M. Long: Characterizations of Learnability for Classes of {O, ..., n}-Valued Functions. 333-340
- Svetlana Anoulova, Paul Fischer, Stefan Pölt, Hans-Ulrich Simon: PAB-Decisions for Boolean and Real-Valued Features. 353-362
- Steffen Lange, Thomas Zeugmann: Types of Monotonic Language Learning and Their Characterization. 377-390
- Yoav Freund: An Improved Boosting Algorithm and Its Implications on Learning Complexity. 391-398
- Neri Merhav, Meir Feder: Universal Sequential Learning and Decision from Individual Data Sequences. 413-427
- Robert H. Sloan: Corrigendum to Types of Noise in Data for Concept Learning. 450
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