16. COLT 2003:
Washington, DC, USA
Bernhard Schölkopf, Manfred K. Warmuth (Eds.):
Computational Learning Theory and Kernel Machines, 16th Annual Conference on Computational Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, USA, August 24-27, 2003, Proceedings.
Lecture Notes in Computer Science 2777 Springer 2003, ISBN 3-540-40720-0 
Target Area:
Computational Game Theory
Invited Talk
Contributed Talks
Kernel Machines
- Corinna Cortes, Patrick Haffner, Mehryar Mohri:
Positive Definite Rational Kernels.
41-56
- Tony Jebara, Risi Kondor:
Bhattacharyya Expected Likelihood Kernels.
57-71
- Matthias Hein, Olivier Bousquet:
Maximal Margin Classification for Metric Spaces.
72-86
- Roni Khardon, Rocco A. Servedio:
Maximum Margin Algorithms with Boolean Kernels.
87-101
- Glenn Fung, Olvi L. Mangasarian, Jude W. Shavlik:
Knowledge-Based Nonlinear Kernel Classifiers.
102-113
- Christina S. Leslie, Rui Kuang:
Fast Kernels for Inexact String Matching.
114-128
- Thomas Gärtner, Peter A. Flach, Stefan Wrobel:
On Graph Kernels: Hardness Results and Efficient Alternatives.
129-143
- Alex J. Smola, Risi Kondor:
Kernels and Regularization on Graphs.
144-158
- Ilya Desyatnikov, Ron Meir:
Data-Dependent Bounds for Multi-category Classification Based on Convex Losses.
159-172
Poster Session 1
Statistical Learning Theory
Online Learning
Other Approaches
Poster Session 2
- Malik Magdon-Ismail, Joseph Sill:
Using a Linear Fit to Determine Monotonicity Directions.
477-491
- Vladimir Koltchinskii, Dmitry Panchenko, Savina Andonova:
Generalization Bounds for Voting Classifiers Based on Sparsity and Clustering.
492-505
- Marcus Hutter:
Sequence Prediction Based on Monotone Complexity.
506-521
- Yuri Kalnishkan, Vladimir Vovk, Michael V. Vyugin:
How Many Strings Are Easy to Predict?
522-536
- Marta Arias, Roni Khardon, Rocco A. Servedio:
Polynomial Certificates for Propositional Classes.
537-551
- Shie Mannor, Nahum Shimkin:
On-Line Learning with Imperfect Monitoring.
552-566
- Shai Ben-David, Reba Schuller:
Exploiting Task Relatedness for Mulitple Task Learning.
567-580
- Eyal Even-Dar, Yishay Mansour:
Approximate Equivalence of Markov Decision Processes.
581-594
- Ran Gilad-Bachrach, Amir Navot, Naftali Tishby:
An Information Theoretic Tradeoff between Complexity and Accuracy.
595-609
- Jeffrey C. Jackson, Rocco A. Servedio:
Learning Random Log-Depth Decision Trees under the Uniform Distribution.
610-624
- Robert H. Sloan, Balázs Szörényi, György Turán:
Projective DNF Formulae and Their Revision.
625-639
- Aharon Bar-Hillel, Daphna Weinshall:
Learning with Equivalence Constraints and the Relation to Multiclass Learning.
640-654
Target Area:
Natural Language Processing
- Michael Collins:
Tutorial: Machine Learning Methods in Natural Language Processing.
655
Invited Talks
- Mehryar Mohri:
Learning from Uncertain Data.
656-670
- Mark Johnson:
Learning and Parsing Stochastic Unification-Based Grammars.
671-683
Inductive Inference Learning
- John Case, Keh-Jiann Chen, Sanjay Jain, Wolfgang Merkle, James S. Royer:
Generality's Price: Inescapable Deficiencies in Machine-Learned Programs.
684-698
- John Case, Sanjay Jain, Franco Montagna, Giulia Simi, Andrea Sorbi:
On Learning to Coordinate: Random Bits Help, Insightful Normal Forms, and Competency Isomorphisms.
699-713
- Sanjay Jain, Efim B. Kinber, Rolf Wiehagen:
Learning All Subfunctions of a Function.
714-728
Open Problems
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