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ORCID5th COLT 1992: Pittsburgh, PA, USA
- David Haussler:
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 - Zhixiang Chen, Wolfgang Maass:
On-line Learning of Rectangles. 16-28 - Michael Kharitonov:
Cryptographic Lower Bounds for Learnability of Boolean Functions on the Uniform Distribution. 29-36 - Jyrki Kivinen, Heikki Mannila, Esko Ukkonen:
Learning Hierarchical Rule Sets. 37-44 - 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 - Jyh-Han Lin, Jeffrey Scott Vitter:
A Theory for Memory-Based Learning. 103-115 - William W. Cohen, Haym Hirsh:
Learnability of Description Logics. 116-127 - Saso Dzeroski, Stephen H. Muggleton, Stuart 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 - John Case, Sanjay Jain, Arun Sharma:
On Learning Limiting Programs. 193-202 - 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 - Rolf Wiehagen, Carl H. Smith:
Generalization versus Classification. 224-230 - Avrim Blum, Prasad Chalasani:
Learning Switching Concepts. 231-242 - Peter L. Bartlett:
Learning With a Slowly Changing Distribution. 243-252 - Gyora M. Benedek, Alon Itai:
Dominating Distributions and Learnability. 253-264 - 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 - H. Sebastian Seung, Manfred Opper, Haim Sompolinsky:
Query by Committee. 287-294 - Yasubumi Sakakibara, Rani Siromoney:
A Noise Model on Learning Sets of Strings. 295-302 - Shyam Kapur, Gianfranco Bilardi:
Language Learning from Stochastic Input. 303-310 - Martin Anthony, Graham R. Brightwell, David A. Cohen, John Shawe-Taylor:
On Exact Specification by Examples. 311-318 - Jeffrey C. Jackson, Andrew Tomkins:
A Computational Model of Teaching. 319-326 - 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 - Michael J. Kearns, Robert E. Schapire, Linda Sellie:
Toward Efficient Agnostic Learning. 341-352 - Svetlana Anoulova, Paul Fischer, Stefan Pölt, Hans Ulrich Simon:
PAB-Decisions for Boolean and Real-Valued Features. 353-362 - Rusins Freivalds, Carl H. Smith:
On the Role of Procrastination for Machine Learning. 363-376 - 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 - David P. Helmbold, Manfred K. Warmuth:
Some Weak Learning Results. 399-412 - Neri Merhav, Meir Feder:
Universal Sequential Learning and Decision from Individual Data Sequences. 413-427 - Klaus-Uwe Höffgen, Hans Ulrich Simon:
Robust Trainability of Single Neurons. 428-439 - Hava T. Siegelmann, Eduardo D. Sontag:
On the Computational Power of Neural Nets. 440-449 - Robert H. Sloan:
Corrigendum to Types of Noise in Data for Concept Learning. 450
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