15. COLT 2002:
Sydney, Australia
Jyrki Kivinen, Robert H. Sloan (Eds.):
Computational Learning Theory, 15th Annual Conference on Computational Learning Theory, COLT 2002, Sydney, Australia, July 8-10, 2002, Proceedings.
Lecture Notes in Computer Science 2375 Springer 2002, ISBN 3-540-43836-X 
- Shahar Mendelson, Robert C. Williamson:
Agnostic Learning Nonconvex Function Classes.
1-13
- Shahar Mendelson, Roman Vershynin:
Entropy, Combinatorial Dimensions and Random Averages.
14-28
- Shahar Mendelson:
Geometric Parameters of Kernel Machines.
29-43
- Peter L. Bartlett, Olivier Bousquet, Shahar Mendelson:
Localized Rademacher Complexities.
44-58
- Olivier Bousquet, Vladimir Koltchinskii, Dmitriy Panchenko:
Some Local Measures of Complexity of Convex Hulls and Generalization Bounds.
59-73
- Eiji Takimoto, Manfred K. Warmuth:
Path Kernels and Multiplicative Updates.
74-89
- Michael V. Vyugin, Vladimir V. V'yugin:
Predictive Complexity and Information.
90-104
- Yuri Kalnishkan, Michael V. Vyugin:
Mixability and the Existence of Weak Complexities.
105-120
- Nicolò Cesa-Bianchi, Alex Conconi, Claudio Gentile:
A Second-Order Perceptron Algorithm.
121-137
- Chris Mesterharm:
Tracking Linear-Threshold Concepts with Winnow.
138-152
- Henning Fernau:
Learning Tree Languages from Text.
153-168
- 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
- Colin de la Higuera, José Oncina:
Inferring Deterministic Linear Languages.
185-200
- Sandra Zilles:
Merging Uniform Inductive Learners.
201-215
- Jürgen Schmidhuber:
The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions.
216-228
- Ke Yang:
New Lower Bounds for Statistical Query Learning.
229-243
- Nader H. Bshouty, Jeffrey C. Jackson, Christino Tamon:
Exploring Learnability between Exact and PAC.
244-254
- Eyal Even-Dar, Shie Mannor, Yishay Mansour:
PAC Bounds for Multi-armed Bandit and Markov Decision Processes.
255-270
- Nader H. Bshouty, Lynn Burroughs:
Bounds for the Minimum Disagreement Problem with Applications to Learning Theory.
271-286
- Nader H. Bshouty, Lynn Burroughs:
On the Proper Learning of Axis Parallel Concepts.
287-302
- Gábor Lugosi, Nicolas Vayatis:
A Consistent Strategy for Boosting Algorithms.
303-318
- Shie Mannor, Ron Meir, Tong Zhang:
The Consistency of Greedy Algorithms for Classification.
319-333
- Gunnar Rätsch, Manfred K. Warmuth:
Maximizing the Margin with Boosting.
334-350
- Sanjoy Dasgupta:
Performance Guarantees for Hierarchical Clustering.
351-363
- Marcus Hutter:
Self-Optimizing and Pareto-Optimal Policies in General Environments Based on Bayes-Mixtures.
364-379
- Lance Fortnow, Jack H. Lutz:
Prediction and Dimension.
380-395
- Christos H. Papadimitriou:
Learning the Internet.
396
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