COLT 1993

55 papers

A Model of Sequence Extrapolation Philip D. Laird, Ronald Saul, Peter Dunning

Acceleration of Learning in Binary Choice Problems Yoshiyuki Kabashima, Shigeru Shinomoto

Amplification of Weak Learning Under the Uniform Distribution Dan Boneh, Richard J. Lipton

Asking Questions to Minimize Errors Nader H. Bshouty, Sally A. Goldman, Thomas R. Hancock, Sleiman Matar

Average Case Analysis of the Clipped Hebb Rule for Nonoverlapping Perception Networks Mostefa Golea, Mario Marchand

Bounding the Vapnik-Chervonenkis Dimension of Concept Classes Parameterized by Real Numbers Paul Goldberg, Mark Jerrum

Capabilities of Fallible FINite Learning Robert P. Daley, Bala Kalyanasundaram, Mahendran Velauthapillai

Capabilities of Probabilistic Learners with Bounded Mind Changes Robert P. Daley, Bala Kalyanasundaram

Choosing a Reliable Hypothesis William S. Evans, Sridhar Rajagopalan, Umesh V. Vazirani

Conservativeness and Monotonicity for Learning Algorithms Eiji Takimoto, Akira Maruoka

Efficient Identification of Regular Expressions from Representative Examples Alvis Brazma

General Bounds on the Number of Examples Needed for Learning Probabilistic Concepts Hans Ulrich Simon

Genetic Algorithms and Machine Learning John J. Grefenstette

Keeping the Neural Networks Simple by Minimizing the Description Length of the Weights Geoffrey E. Hinton, Drew van Camp

Language Learning in Dependence on the Space of Hypotheses Steffen Lange, Thomas Zeugmann

Learning and Robust Learning of Product Distributions Klaus-Uwe Höffgen

Learning Binary Relations Using Weighted Majority Voting Sally A. Goldman, Manfred K. Warmuth

Learning Fallible Finite State Automata Dana Ron, Ronitt Rubinfeld

Learning from a Population of Hypotheses Michael J. Kearns, H. Sebastian Seung

Learning Kµ Decision Trees on the Uniform Distribution Thomas R. Hancock

Learning Sparse Multivariate Polynomials over a Field with Queries and Counterexamples Robert E. Schapire, Linda Sellie

Learning Two-Tape Automata from Queries and Counterexamples Takashi Yokomori

Learning Unions of Two Rectangles in the Plane with Equivalence Queries Zhixiang Chen

Learning with Restricted Focus of Attention Shai Ben-David, Eli Dichterman

Learning Μ-Branching Programs with Queries Vijay Raghavan, Dawn Wilkins

Linear Time Deterministic Learning of K-Term DNF Ulf Berggren

Localization vs. Identification of Semi-Algebraic Sets Shai Ben-David, Michael Lindenbaum

Lower Bounds for PAC Learning with Queries György Turán

Lower Bounds on the Vapnik-Chervonenkis Dimension of Multi-Layer Threshold Networks Peter L. Bartlett

Occam's Razor for Functions B. K. Natarajan

On Learning Embedded Symmetric Concepts Avrim Blum, Prasad Chalasani, Jeffrey C. Jackson

On Learning in the Limit and Non-Uniform (epsilon, Delta)-Learning Shai Ben-David, Michal Jacovi

On Learning Multiple Concepts in Parallel Efim B. Kinber, Carl H. Smith, Mahendran Velauthapillai, Rolf Wiehagen

On Learning Visual Concepts and DNF Formulae Eyal Kushilevitz, Dan Roth

On Polynomial-Time Probably Almost Discriminative Learnability Kenji Yamanishi

On Probably Correct Classification of Concepts Sanjeev R. Kulkarni, Ofer Zeitouni

On the Average Tractability of Binary Integer Programming and the Curious Transition to Perfect Generalization in Learning Majority Functions Shao C. Fang, Santosh S. Venkatesh

On the Complexity of Function Learning Peter Auer, Philip M. Long, Wolfgang Maass, Gerhard J. Woeginger

On the Impact of Forgetting on Learning Machines Rusins Freivalds, Efim B. Kinber, Carl H. Smith

On the Power of Polynomial Discriminators and Radial Basis Function Networks Martin Anthony, Sean B. Holden

On the Power of Sigmoid Neural Networks Joe Kilian, Hava T. Siegelmann

On the Query Complexity of Learning Sampath Kannan

On the Structure of Degrees of Inferability Martin Kummer, Frank Stephan

On-Line Learning of Functions of Bounded Variation Under Various Sampling Schemes S. E. Posner, Sanjeev R. Kulkarni

On-Line Learning of Rectangles in Noisy Environments Peter Auer

On-Line Learning with Linear Loss Constraints Nick Littlestone, Philip M. Long

Parameterized Learning Complexity Rodney G. Downey, Patricia A. Evans, Michael R. Fellows

Piecemeal Learning of an Unknown Environment Margrit Betke, Ronald L. Rivest, Mona Singh

Polynomial Learnability of Linear Threshold Approximations Tom Bylander

Probability Is More Powerful than Team for Language Identification from Positive Data Sanjay Jain, Arun Sharma

Rate of Approximation Results Motivated by Robust Neural Network Learning Christian Darken, Michael Donahue, Leonid Gurvits, Eduardo D. Sontag

Statistical Queries and Faulty PAC Oracles Scott E. Decatur

Teaching a Smart Learner Sally A. Goldman, H. David Mathias

The "lob-Pass" Problem and an On-Line Learning Model of Rational Choice Naoki Abe, Jun'ichi Takeuchi

Worst-Case Quadratic Loss Bounds for a Generalization of the Widrow-Hoff Rule Nicolò Cesa-Bianchi, Philip M. Long, Manfred K. Warmuth