15. COLT 2002: Sydney, Australia

@proceedings{DBLP:conf/colt/2002,
  editor    = {Jyrki Kivinen and
               Robert H. Sloan},
  title     = {Computational Learning Theory, 15th Annual Conference on Computational
               Learning Theory, COLT 2002, Sydney, Australia, July 8-10, 2002,
               Proceedings},
  booktitle = {COLT},
  publisher = {Springer},
  series    = {Lecture Notes in Computer Science},
  volume    = {2375},
  year      = {2002},
  isbn      = {3-540-43836-X},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

• Shahar Mendelson, Robert C. Williamson:
  Agnostic Learning Nonconvex Function Classes. 1-13
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• Shahar Mendelson, Roman Vershynin:
  Entropy, Combinatorial Dimensions and Random Averages. 14-28
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• Shahar Mendelson:
  Geometric Parameters of Kernel Machines. 29-43
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• Peter L. Bartlett, Olivier Bousquet, Shahar Mendelson:
  Localized Rademacher Complexities. 44-58
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• Olivier Bousquet, Vladimir Koltchinskii, Dmitriy Panchenko:
  Some Local Measures of Complexity of Convex Hulls and Generalization Bounds. 59-73
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• Eiji Takimoto, Manfred K. Warmuth:
  Path Kernels and Multiplicative Updates. 74-89
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• Michael V. Vyugin, Vladimir V. V'yugin:
  Predictive Complexity and Information. 90-104
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• Yuri Kalnishkan, Michael V. Vyugin:
  Mixability and the Existence of Weak Complexities. 105-120
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• Nicolò Cesa-Bianchi, Alex Conconi, Claudio Gentile:
  A Second-Order Perceptron Algorithm. 121-137
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• Chris Mesterharm:
  Tracking Linear-Threshold Concepts with Winnow. 138-152
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• Henning Fernau:
  Learning Tree Languages from Text. 153-168
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• Yusuke Suzuki, Ryuta Akanuma, Takayoshi Shoudai, Tetsuhiro Miyahara, Tomoyuki Uchida:
  Polynomial Time Inductive Inference of Ordered Tree Patterns with Internal Structured Variables from Positive Data. 169-184
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• Colin de la Higuera, José Oncina:
  Inferring Deterministic Linear Languages. 185-200
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• Sandra Zilles:
  Merging Uniform Inductive Learners. 201-215
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• Jürgen Schmidhuber:
  The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions. 216-228
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• Ke Yang:
  New Lower Bounds for Statistical Query Learning. 229-243
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• Nader H. Bshouty, Jeffrey C. Jackson, Christino Tamon:
  Exploring Learnability between Exact and PAC. 244-254
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• Eyal Even-Dar, Shie Mannor, Yishay Mansour:
  PAC Bounds for Multi-armed Bandit and Markov Decision Processes. 255-270
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• Nader H. Bshouty, Lynn Burroughs:
  Bounds for the Minimum Disagreement Problem with Applications to Learning Theory. 271-286
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• Nader H. Bshouty, Lynn Burroughs:
  On the Proper Learning of Axis Parallel Concepts. 287-302
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• Gábor Lugosi, Nicolas Vayatis:
  A Consistent Strategy for Boosting Algorithms. 303-318
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• Shie Mannor, Ron Meir, Tong Zhang:
  The Consistency of Greedy Algorithms for Classification. 319-333
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• Gunnar Rätsch, Manfred K. Warmuth:
  Maximizing the Margin with Boosting. 334-350
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• Sanjoy Dasgupta:
  Performance Guarantees for Hierarchical Clustering. 351-363
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• Marcus Hutter:
  Self-Optimizing and Pareto-Optimal Policies in General Environments Based on Bayes-Mixtures. 364-379
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• Lance Fortnow, Jack H. Lutz:
  Prediction and Dimension. 380-395
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• Christos H. Papadimitriou:
  Learning the Internet. 396
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