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ORCID13th COLT 2000: Palo Alto, California, USA
- Nicolò Cesa-Bianchi, Sally A. Goldman:
Proceedings of the Thirteenth Annual Conference on Computational Learning Theory (COLT 2000), June 28 - July 1, 2000, Palo Alto, California, USA. Morgan Kaufmann 2000, ISBN 1-55860-703-X
Session 1
- David A. McAllester, Robert E. Schapire:
On the Convergence Rate of Good-Turing Estimators. COLT 2000: 1-6 - Jeffrey C. Jackson:
On the Efficiency of Noise-Tolerant PAC Algorithms Derived from Statistical Queries. COLT 2000: 7-15 - Dinesh P. Mehta, Vijay Raghavan:
Decision Tree Approximations of Boolean Functions. COLT 2000: 16-24 - John Langford, David A. McAllester:
Computable Shell Decomposition Bounds. COLT 2000: 25-34
Session 2
- Koby Crammer, Yoram Singer:
On the Learnability and Design of Output Codes for Multiclass Problems. COLT 2000: 35-46 - Sanjay Jain, Efim B. Kinber, Rolf Wiehagen:
Language Learning From Texts: Degrees of Instrinsic Complexity and Their Characterizations. COLT 2000: 47-58 - Frank Stephan, Thomas Zeugmann:
Average-Case Complexity of Learning Polynomials. COLT 2000: 59-68
Session 3
- Yishay Mansour, David A. McAllester:
Generalization Bounds for Decision Trees. COLT 2000: 69-74 - Nicolas Vayatis:
The Role of Critical Sets in Vapnik-Chervonenkis Theory. COLT 2000: 75-80 - Shahar Mendelson, Naftali Tishby:
Statistical Sufficiency for Classes in Empirical L2 Spaces. COLT 2000: 81-89
Session 4
- Jürgen Forster, Manfred K. Warmuth:
Relative Expected Instantaneous Loss Bounds. COLT 2000: 90-99 - Eiji Takimoto, Manfred K. Warmuth:
The Minimax Strategy for Gaussian Density Estimation. pp. COLT 2000: 100-106 - Peter Auer, Claudio Gentile:
Adaptive and Self-Confident On-Line Learning Algorithms. COLT 2000: 107-117 - Peter Auer:
An Improved On-line Algorithm for Learning Linear Evaluation Functions. COLT 2000: 118-125
Session 5
- Yoav Freund, Manfred Opper:
Continuous Drifting Games. COLT 2000: 126-132 - Peter L. Bartlett, Jonathan Baxter:
Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning. COLT 2000: 133-141 - Michael J. Kearns, Satinder P. Singh:
Bias-Variance Error Bounds for Temporal Difference Updates. COLT 2000: 142-147
Session 6
- Rocco A. Servedio:
PAC Analogues of Perceptron and Winnow via Boosting the Margin. COLT 2000: 148-157 - Michael Collins, Robert E. Schapire, Yoram Singer:
Logistic Regression, AdaBoost and Bregman Distances. COLT 2000: 158-169 - Gunnar Rätsch, Manfred K. Warmuth, Sebastian Mika, Takashi Onoda, Steven Lemm, Klaus-Robert Müller:
Barrier Boosting. COLT 2000: 170-179 - Carlos Domingo, Osamu Watanabe:
MadaBoost: A Modification of AdaBoost. COLT 2000: 180-189
Session 7
- Ron Meir, Ran El-Yaniv, Shai Ben-David:
Localized Boosting. COLT 2000: 190-199 - Javed A. Aslam:
Improving Algorithms for Boosting. COLT 2000: 200-207 - Nigel Duffy, David P. Helmbold:
Leveraging for Regression. COLT 2000: 208-219 - Yishay Mansour, David A. McAllester:
Boosting Using Branching Programs. COLT 2000: 220-224
Session 8
- Paul W. Goldberg, Stephen Kwek:
The Precision of Query Points as a Resource for Learning Convex Polytopes with Membership Queries. COLT 2000: 225-235 - Judy Goldsmith, Robert H. Sloan, Balázs Szörényi, György Turán:
Improved Algorithms for Theory Revision with Queries. COLT 2000: 236-247 - José L. Balcázar, Jorge Castro, David Guijarro:
Abstract Combinatorial Characterizations of Exact Learning via Queries. COLT 2000: 248-254
Session 9
- Shai Ben-David, Nadav Eiron, Hans Ulrich Simon:
The Computational Complexity of Densest Region Detection. COLT 2000: 255-265 - Shai Ben-David, Nadav Eiron, Philip M. Long:
On the Difficulty of Approximately Maximizing Agreements. COLT 2000: 266-274 - Christian Kuhlmann:
Hardness Results for General Two-Layer Neural Networks. COLT 2000: 275-285
Session 10
- Peter L. Bartlett, Stéphane Boucheron, Gábor Lugosi:
Model Selection and Error Estimation. COLT 2000: 286-297 - Thore Graepel, Ralf Herbrich, John Shawe-Taylor:
Generalisation Error Bounds for Sparse Linear Classifiers. COLT 2000: 298-303 - Ralf Herbrich, Thore Graepel, John Shawe-Taylor:
Sparsity vs. Large Margins for Linear Classifiers. COLT 2000: 304-308 - Robert C. Williamson, Alexander J. Smola, Bernhard Schölkopf:
Entropy Numbers of Linear Function Classes. COLT 2000: 309-319
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