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• 
  Yoav Freund, Robert E. Schapire:
  Proceedings of the Tenth Annual Conference on Computational Learning Theory, COLT 1997, Nashville, Tennessee, USA, July 6-9, 1997. ACM 1997, ISBN 0-89791-891-6

• 
  Andrew R. Barron:
  Information Theory in Probability, Statistics, Learning, and Neural Nets (Abstract). COLT 1997: 1

• 
  John Shawe-Taylor, Robert C. Williamson:
  A PAC Analysis of a Bayesian Estimator. 2-9
• 
  Tamás Horváth, Robert H. Sloan, György Turán:
  Learning Logic Programs by Using the Product Homomorphism Method. 10-20

• 
  Philip M. Long:
  On-line Evaluation and Prediction using Linear Functions. 21-31
• 
  V. G. Vovk:
  Derandomizing Stochastic Prediction Strategies. 32-44
• 
  Avrim Blum, Carl Burch:
  On-line Learning and the Metrical Task System Problem. 45-53

• 
  Wolfgang Maass, Michael Schmitt:
  On the Complexity of Learning for a Spiking Neuron (Extended Abstract). 54-61
• 
  Tom Bylander:
  Learning Probabilistically Consistent Linear Threshold Functions. 62-71
• 
  Claude-Nicolas Fiechter:
  PAC Adaptive Control of Linear Systems. 72-80

• 
  Robert P. Daley, Bala Kalyanasundaram:
  FINite Learning Capabilities and Their Limits. 81-89
• 
  Kalvis Apsitis, Rusins Freivalds, Carl H. Smith:
  Asymmetric Team Learning. 90-95
• 
  Arun Sharma, Frank Stephan, Yuri Ventsov:
  Generalized Notions of Mind Change Complexity. 96-108

• 
  David Haussler:
  A Brief Look at Some Machine Learning Problems in Genomics. 109-113

• 
  Fernando C. N. Pereira, Yoram Singer:
  An Efficient Extension to Mixture Techniques for Prediction and Decision Trees. 114-121
• 
  Ron Meir:
  Performance Bounds for Nonlinear Time Series Prediction. 122-129

• 
  Scott E. Decatur, Oded Goldreich, Dana Ron:
  Computational Sample Complexity. 130-142
• 
  Adam Kowalczyk:
  Dense Shattering and Teaching Dimensions for Differentiable Families (Extended Abstract). 143-151
• 
  Michael J. Kearns, Dana Ron:
  Algorithmic Stability and Sanity-Check Bounds for Leave-one-Out Cross-Validation. 152-162

• 
  Nicolò Cesa-Bianchi:
  Analysis of Two Gradient-based Algorithms for On-line Regression. 163-170
• 
  Adam J. Grove, Nick Littlestone, Dale Schuurmans:
  General Convergence Results for Linear Discriminant Updates. 171-183
• 
  Tom Bylander:
  The Binary Exponentiated Gradient Algorithm for Learning Linear Functions. 184-192

• 
  Víctor Dalmau:
  A Dichotomy Theorem for Learning Quantified Boolean Formulas. 193-200
• 
  Yishay Mansour, Mariano Schain:
  Learning with Maximum-Entropy Distributions. 201-210
• 
  Nina Mishra, Leonard Pitt:
  Generating all Maximal Independent Sets of Bounded-Degree Hypergraphs. 211-217

• 
  David P. Helmbold, Sandra Panizza:
  Some Label Efficient Learning Results. 218-230
• 
  Sally A. Goldman, Stephen Kwek, Stephen D. Scott:
  Learning from Examples with Unspecified Attribute Values (Extended Abstract). 231-242
• 
  Funda Ergün, Ravi Kumar, Ronitt Rubinfeld:
  Learning Distributions from Random Walks. 243-249
• 
  Kenji Yamanishi:
  Distributed Cooperative Bayesian Learning Strategies. 250-262

• 
  Susanne Kaufmann, Frank Stephan:
  Resource Bounded Next Value and Explanatory Identification: Learning Automata, Patterns and Polynomials On-Line. 263-274
• 
  William I. Gasarch, Andrew C. Y. Lee:
  Inferring Answers to Queries. 275-284
• 
  Dana Angluin, Martins Krikis:
  Teachers, Learners and Black Boxes. 285-297

• 
  Dana Angluin, Miklós Csürös:
  Learning Markov Chains with Variable Memory Length from Noisy Output. 298-308
• 
  Avrim Blum, Adam Kalai:
  Universal Portfolios With and Without Transaction Costs. 309-313
• 
  Dimitris Bertsimas, David Gamarnik, John N. Tsitsiklis:
  Estimation of Time-Varying Parameters in Statistical Models: An Optimization Approach. 314-324
• 
  Sally A. Goldman, Stephen Kwek, Stephen D. Scott:
  Agnostic Learning of Geometric Patterns (Extended Abstract). 325-333

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last updated on 2024-04-18 20:19 CEST by the dblp team

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