6. COLT 1993:
Santa Cruz, CA, USA
Lenny Pitt (Ed.):
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 V. 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 W. 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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