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ORCID6th COLT 1993: Santa Cruz, CA, USA
- Lenny Pitt:
Proceedings of the Sixth Annual ACM Conference on Computational Learning Theory, COLT 1993, Santa Cruz, CA, USA, July 26-28, 1993. ACM 1993, ISBN 0-89791-611-5 - John J. Grefenstette:
Genetic Algorithms and Machine Learning. 3-4 - Geoffrey E. Hinton, Drew van Camp:
Keeping the Neural Networks Simple by Minimizing the Description Length of the Weights. 5-13 - Robert E. Schapire, Linda Sellie:
Learning Sparse Multivariate Polynomials over a Field with Queries and Counterexamples. 17-26 - Vijay Raghavan, Dawn Wilkins:
Learning µ-branching Programs with Queries. 27-36 - Ulf Berggren:
Linear Time Deterministic Learning of k-Term DNF. 37-40 - Nader H. Bshouty, Sally A. Goldman, Thomas R. Hancock, Sleiman Matar:
Asking Questions to Minimize Errors. 41-50 - Rodney G. Downey, Patricia A. Evans, Michael R. Fellows:
Parameterized Learning Complexity. 51-57 - Sampath Kannan:
On the Query Complexity of Learning. 58-66 - Sally A. Goldman, H. David Mathias:
Teaching a Smart Learner. 67-76 - Klaus-Uwe Höffgen:
Learning and Robust Learning of Product Distributions. 77-83 - Philip D. Laird, Ronald Saul, Peter Dunning:
A Model of Sequence Extrapolation. 84-93 - Kenji Yamanishi:
On Polynomial-Time Probably almost Discriminative Learnability. 94-100 - Michael J. Kearns, H. Sebastian Seung:
Learning from a Population of Hypotheses. 101-110 - Sanjeev R. Kulkarni, Ofer Zeitouni:
On Probably Correct Classification of Concepts. 111-116 - Martin Kummer, Frank Stephan:
On the Structure of Degrees of Inferability. 117-126 - Steffen Lange, Thomas Zeugmann:
Language Learning in Dependence on the Space of Hypotheses. 127-136 - Joe Kilian, Hava T. Siegelmann:
On the Power of Sigmoid Neural Networks. 137-143 - Peter L. Bartlett:
Lower Bounds on the Vapnik-Chervonenkis Dimension of Multi-Layer Threshold Networks. 144-150 - Mostefa Golea, Mario Marchand:
Average Case Analysis of the Clipped Hebb Rule for Nonoverlapping Perception Networks. 151-157 - Martin Anthony
, Sean B. Holden:
On the Power of Polynomial Discriminators and Radial Basis Function Networks. 158-164 - Rusins Freivalds, Efim B. Kinber, Carl H. Smith:
On the Impact of Forgetting on Learning Machines. 165-174 - Efim B. Kinber, Carl H. Smith, Mahendran Velauthapillai, Rolf Wiehagen:
On Learning Multiple Concepts in Parallel. 175-181 - Robert P. Daley, Bala Kalyanasundaram:
Capabilities of Probabilistic Learners with Bounded Mind Changes. 182-191 - Sanjay Jain, Arun Sharma:
Probability is More Powerful Than Team for Language Identification from Positive Data. 192-198 - Robert P. Daley, Bala Kalyanasundaram, Mahendran Velauthapillai:
Capabilities of fallible FINite Learning. 199-208 - Shai Ben-David, Michal Jacovi:
On Learning in the Limit and Non-Uniform (epsilon, delta)-Learning. 209-217 - Dana Ron, Ronitt Rubinfeld:
Learning Fallible Finite State Automata. 218-227 - Takashi Yokomori:
Learning Two-Tape Automata from Queries and Counterexamples. 228-235 - Alvis Brazma:
Efficient Identification of Regular Expressions from Representative Examples. 236-242 - Zhixiang Chen:
Learning Unions of Two Rectangles in the Plane with Equivalence Queries. 243-252 - Peter Auer:
On-Line Learning of Rectangles in Noisy Environments. 253-261 - Scott E. Decatur:
Statistical Queries and Faulty PAC Oracles. 262-268 - William S. Evans, Sridhar Rajagopalan, Umesh V. Vazirani:
Choosing a Reliable Hypothesis. 269-276 - Margrit Betke, Ronald L. Rivest, Mona Singh:
Piecemeal Learning of an Unknown Environment. 277-286 - Shai Ben-David, Eli Dichterman:
Learning with Restricted Focus of Attention. 287-296 - Tom Bylander:
Polynomial Learnability of Linear Threshold Approximations. 297-302 - Christian Darken, Michael Donahue, Leonid Gurvits, Eduardo D. Sontag:
Rate of Approximation Results Motivated by Robust Neural Network Learning. 303-309 - Shao C. Fang, Santosh S. Venkatesh:
On the Average Tractability of Binary Integer Programming and the Curious Transition to Perfect Generalization in Learning Majority Functions. 310-316 - Eyal Kushilevitz, Dan Roth:
On Learning Visual Concepts and DNF Formulae. 317-326 - Shai Ben-David, Michael Lindenbaum:
Localization vs. Identification of Semi-Algebraic Sets. 327-336 - Avrim Blum, Prasad Chalasani, Jeffrey C. Jackson:
On Learning Embedded Symmetric Concepts. 337-346 - Dan Boneh, Richard J. Lipton:
Amplification of Weak Learning under the Uniform Distribution. 347-351 - Thomas R. Hancock:
Learning kµ Decision Trees on the Uniform Distribution. 352-360 - Paul Goldberg, Mark Jerrum:
Bounding the Vapnik-Chervonenkis Dimension of Concept Classes Parameterized by Real Numbers. 361-369 - B. K. Natarajan:
Occam's Razor for Functions. 370-376 - Eiji Takimoto, Akira Maruoka:
Conservativeness and Monotonicity for Learning Algorithms. 377-383 - György Turán:
Lower Bounds for PAC Learning with Queries. 384-391 - Peter Auer, Philip M. Long, Wolfgang Maass, Gerhard J. Woeginger:
On the Complexity of Function Learning. 392-401 - Hans Ulrich Simon:
General Bounds on the Number of Examples Needed for Learning Probabilistic Concepts. 402-411 - Nick Littlestone, Philip M. Long:
On-Line Learning with Linear Loss Constraints. 412-421 - Naoki Abe, Jun'ichi Takeuchi:
The "lob-pass" Problem and an On-line Learning Model of Rational Choice. 422-428 - Nicolò Cesa-Bianchi, Philip M. Long, Manfred K. Warmuth:
Worst-Case Quadratic Loss Bounds for a Generalization of the Widrow-Hoff Rule. 429-438 - S. E. Posner, Sanjeev R. Kulkarni:
On-Line Learning of Functions of Bounded Variation under Various Sampling Schemes. 439-445 - Yoshiyuki Kabashima, Shigeru Shinomoto:
Acceleration of Learning in Binary Choice Problems. 446-452 - Sally A. Goldman, Manfred K. Warmuth:
Learning Binary Relations Using Weighted Majority Voting. 453-462
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