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ORCID9th COLT 1996: Desenzano del Garda, Italy
- Avrim Blum, Michael J. Kearns:
Proceedings of the Ninth Annual Conference on Computational Learning Theory, COLT 1996, Desenzano del Garda, Italy, June 28-July 1, 1996. ACM 1996, ISBN 0-89791-811-8 - David D. Lewis:
Challenges in Machine Learning for Text Classification. 1 - Leslie Ann Goldberg:
Analysis of a Simple Learning Algorithm: Learning Foraging Thresholds for Lizards. 2-9 - Anthony M. Zador, Barak A. Pearlmutter
:
VC Dimension of an Integrate-and-Fire Neuron Model. 10-18 - Sven Koenig, Yury V. Smirnov:
Graph Learning with a Nearest Neighbor Approach. 19-28 - William W. Cohen:
The Dual DFA Learning Problem: Hardness Results for Programming by Demonstration and Learning First-Order Representations (Extended Abstract). 29-40 - Sean B. Holden:
PAC-Like Upper Bounds for the Sample Complexity of Leave-one-Out Cross-Validation. 41-50 - Gábor Lugosi, Márta Pintér:
A Data-Dependent Skeleton Estimate for Learning. 51-56 - Joel Ratsaby, Ron Meir, Vitaly Maiorov:
Towards Robust Model Selection Using Estimation and Approximation Error Bounds. 57-67 - John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson, Martin Anthony:
A Framework for Structural Risk Minimisation. 68-76 - Jonathan Baxter:
A Bayesian/Information Theoretic Model of Bias Learning. 77-88 - Yoav Freund:
Predicting a Binary Sequence Almost As Well As the Optimal Biased Coin. 89-98 - Kenji Yamanishi:
A Randomized Approximation of the MDL for Stochastic Models with Hidden Variables. 99-109 - V. G. Vovk:
Learning an Optimal Decision Strategy in an Influence Diagram with Latent Variables. 110-121 - Rakesh D. Barve, Philip M. Long:
On the Complexity of Learning from Drifting Distributions. 122-130 - Peter L. Bartlett, Shai Ben-David, Sanjeev R. Kulkarni:
Learning Changing Concepts by Exploiting the Structure of Change. 131-139 - Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson:
The Importance of Convexity in Learning with Squared Loss. 140-146 - Lawrence K. Saul, Satinder P. Singh:
Learning Curve Bounds for a Markov Decision Process with Undiscounted Rewards. 147-156 - Andris Ambainis:
Probabilistic and Team PFIN-Type Learning: General Properties. 157-168 - Ganesh Baliga, John Case, Sanjay Jain:
Synthesizing Enumeration Techniques for Language Learning. 169-180 - Sanjay Jain, Arun Sharma:
Elementary Formal Systems, Intrinsic Complexity, and Procrastination. 181-192 - Dick De Jongh, Makoto Kanazawa:
Angluin's Theorem for Indexed Families of r.e. Sets and Applications. 193-204 - Andreas Birkendorf, Eli Dichterman, Jeffrey C. Jackson, Norbert Klasner, Hans Ulrich Simon:
On Restricted-Focus-of-Attention Learnability of Boolean Functions. 205-216 - Igal Galperin:
Analysis of Greedy Expert Hiring and an Application to Memory-Based Learning (Extended Abstract). 217-223 - Nader H. Bshouty, Christino Tamon, David K. Wilson:
On Learning width Two Branching Programs (Extended Abstract). 224-227 - Philip M. Long, Lei Tan:
PAC Learning Axis-Aligned Rectangles with Respect to Product Distributions from Multiple-Instance Examples. 228-234 - Nader H. Bshouty, Lisa Hellerstein:
Attribute-Efficient Learning in Query and Mistake-Bound Models. 235-243 - Stephen Kwek, Leonard Pitt:
PAC Learning Intersections of Halfspaces with Membership Queries (Extended Abstract). 244-254 - Aaron Feigelson, Lisa Hellerstein:
Learning Conjunctions of Two Unate DNF Formulas (Extended Abstract): Computational and Informational Results. 255-265 - Eyal Kushilevitz:
A Simple Algorithm for Learning O(log n)-Term DNF. 266-269 - Wolfgang Merkle, Frank Stephan:
Trees and Learning. 270-279 - Martin Kummer, Matthias Ott:
Learning Branches and Learning to Win Closed Games. 280-291 - Christopher D. Rosin, Richard K. Belew:
A Competitive Approach to Game Learning. 292-302 - András Antos, Gábor Lugosi:
Strong Minimax Lower Bounds for Learning. 303-309 - Erik Ordentlich, Thomas M. Cover:
On-Line Portfolio Selection. 310-313 - Nicolò Cesa-Bianchi, David P. Helmbold, Sandra Panizza:
On Bayes Methods for On-Line Boolean Prediction. 314-324 - Yoav Freund, Robert E. Schapire:
Game Theory, On-Line Prediction and Boosting. 325-332 - 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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