9. COLT 1996: Desenzano del Garda, Italy

• David D. Lewis:
  Challenges in Machine Learning for Text Classification. 1
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• Leslie Ann Goldberg:
  Analysis of a Simple Learning Algorithm: Learning Foraging Thresholds for Lizards. 2-9
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• Anthony M. Zador, Barak A. Pearlmutter:
  VC Dimension of an Integrate-and-Fire Neuron Model. 10-18
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• Sven Koenig, Yury V. Smirnov:
  Graph Learning with a Nearest Neighbor Approach. 19-28
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• William W. Cohen:
  The Dual DFA Learning Problem: Hardness Results for Programming by Demonstration and Learning First-Order Representations (Extended Abstract). 29-40
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• Sean B. Holden:
  PAC-Like Upper Bounds for the Sample Complexity of Leave-one-Out Cross-Validation. 41-50
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• Gábor Lugosi, Márta Pintér:
  A Data-Dependent Skeleton Estimate for Learning. 51-56
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• Joel Ratsaby, Ron Meir, Vitaly Maiorov:
  Towards Robust Model Selection Using Estimation and Approximation Error Bounds. 57-67
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• John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson, Martin Anthony:
  A Framework for Structural Risk Minimisation. 68-76
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• Jonathan Baxter:
  A Bayesian/Information Theoretic Model of Bias Learning. 77-88
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• Yoav Freund:
  Predicting a Binary Sequence Almost As Well As the Optimal Biased Coin. 89-98
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• Kenji Yamanishi:
  A Randomized Approximation of the MDL for Stochastic Models with Hidden Variables. 99-109
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• V. G. Vovk:
  Learning an Optimal Decision Strategy in an Influence Diagram with Latent Variables. 110-121
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• Rakesh D. Barve, Philip M. Long:
  On the Complexity of Learning from Drifting Distributions. 122-130
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• Peter L. Bartlett, Shai Ben-David, Sanjeev R. Kulkarni:
  Learning Changing Concepts by Exploiting the Structure of Change. 131-139
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• Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson:
  The Importance of Convexity in Learning with Squared Loss. 140-146
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• Lawrence K. Saul, Satinder P. Singh:
  Learning Curve Bounds for a Markov Decision Process with Undiscounted Rewards. 147-156
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• Andris Ambainis:
  Probabilistic and Team PFIN-Type Learning: General Properties. 157-168
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• Ganesh Baliga, John Case, Sanjay Jain:
  Synthesizing Enumeration Techniques for Language Learning. 169-180
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• Sanjay Jain, Arun Sharma:
  Elementary Formal Systems, Intrinsic Complexity, and Procrastination. 181-192
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• Dick De Jongh, Makoto Kanazawa:
  Angluin's Theorem for Indexed Families of r.e. Sets and Applications. 193-204
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• Andreas Birkendorf, Eli Dichterman, Jeffrey C. Jackson, Norbert Klasner, Hans-Ulrich Simon:
  On Restricted-Focus-of-Attention Learnability of Boolean Functions. 205-216
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• Igal Galperin:
  Analysis of Greedy Expert Hiring and an Application to Memory-Based Learning (Extended Abstract). 217-223
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• Nader H. Bshouty, Christino Tamon, David K. Wilson:
  On Learning width Two Branching Programs (Extended Abstract). 224-227
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• Philip M. Long, Lei Tan:
  PAC Learning Axis-Aligned Rectangles with Respect to Product Distributions from Multiple-Instance Examples. 228-234
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• Nader H. Bshouty, Lisa Hellerstein:
  Attribute-Efficient Learning in Query and Mistake-Bound Models. 235-243
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• Stephen Kwek, Leonard Pitt:
  PAC Learning Intersections of Halfspaces with Membership Queries (Extended Abstract). 244-254
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• Aaron Feigelson, Lisa Hellerstein:
  Learning Conjunctions of Two Unate DNF Formulas (Extended Abstract): Computational and Informational Results. 255-265
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• Eyal Kushilevitz:
  A Simple Algorithm for Learning O(log n)-Term DNF. 266-269
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• Wolfgang Merkle, Frank Stephan:
  Trees and Learning. 270-279
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• Martin Kummer, Matthias Ott:
  Learning Branches and Learning to Win Closed Games. 280-291
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• Christopher D. Rosin, Richard K. Belew:
  A Competitive Approach to Game Learning. 292-302
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• András Antos, Gábor Lugosi:
  Strong Minimax Lower Bounds for Learning. 303-309
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• Erik Ordentlich, Thomas M. Cover:
  On-Line Portfolio Selection. 310-313
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• Nicolò Cesa-Bianchi, David P. Helmbold, Sandra Panizza:
  On Bayes Methods for On-Line Boolean Prediction. 314-324
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• Yoav Freund, Robert E. Schapire:
  Game Theory, On-Line Prediction and Boosting. 325-332
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• Peter Auer, Stephen Kwek, Wolfgang Maass, Manfred K. Warmuth:
  Learning of Depth Two Neural Networks with Constant Fan-In at the Hidden Nodes (Extended Abstract). 333-343
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