8. COLT 1995: Santa Cruz, California, USA
Wolfgang Maass (Ed.): Proceedings of the Eigth Annual Conference on Computational Learning Theory, COLT 1995, Santa Cruz, California, USA, July 5-8, 1995. ACM 1995 ISBN 0-89791-723-5
Invited Talks
- Leslie G. Valiant: Rationality. 3-14
Session 1
- Michael J. Kearns, Yishay Mansour, Andrew Y. Ng, Dana Ron: An Experimental and Theoretical Comparison of Model Selection Methods. 21-30
- Dana Ron, Yoram Singer, Naftali Tishby: On the Learnability and Usage of Acyclic Probabilistic Finite Automata. 31-40
Session 2
- V. G. Vovk: A Game of Prediction with Expert Advice. 51-60
- David P. Helmbold, Robert E. Schapire: Predicting Nearly as Well as the Best Pruning of a Decision Tree. 61-68
- David P. Helmbold, Yoram Singer, Robert E. Schapire, Manfred K. Warmuth: A Comparison of New and Old Algorithms for a Mixture Estimation Problem. 69-78
- Nader H. Bshouty: A Note on Learning Multivariate Polynomials Under the Uniform Distribution (Extended Abstract). 79-82
- Kenji Yamanishi: Randomized Approximate Aggregating Strategies and Their Applications to Prediction and Discrimination. 83-90
- Olga Mitina, Nikolai K. Vereshchagin: How to Use Expert Advice in the Case when Actual Values of Estimated Events Remain Unknown. 91-97
Session 3
- Avrim Blum, Prasad Chalasani, Sally A. Goldman, Donna K. Slonim: Learning with Unreliable Boundary Queries. 98-107
- Tibor Hegedüs: Generalized Teaching Dimensions and the Query Complexity of Learning. 108-117
- Nader H. Bshouty, Jeffrey C. Jackson: Learning DNF over the Uniform Distribution using a Quantum Example Oracle. 118-127
- Yiqun Lisa Yin: Reducing the Number of Queries in Self-Directed Learning. 128-135
Session 4
- Frank Stephan: Learning via Queries and Oracles. 162-169
- Kalvis Apsitis, Rusins Freivalds, Carl H. Smith: On the Inductive Inference of Real Valued Functions. 170-177
- Efim B. Kinber, Frank Stephan: Language Learning from Texts: Mind Changes, Limited Memory and Monotonicity (Extended Abstract). 182-189
- Nader H. Bshouty, Christino Tamon, David K. Wilson: On Learning Decision Trees with Large Output Domains (Extended Abstract). 190-197
- Nader H. Bshouty, Zhixiang Chen, Scott E. Decatur, Steven Homer: On the Learnability of Zn-DNF Formulas (Extended Abstract). 198-205
- Yoshifumi Sakai, Eiji Takimoto, Akira Maruoka: Proper Learning Algorithm for Functions of k Terms Under Smooth Distributions. 206-213
- Atsuyoshi Nakamura, Naoki Abe: On-line Learning of Binary and n-ary Relations over Multi-dimensional Clusters. 214-221
- H. David Mathias: DNF - If You Can't Learn'em, Teach'em: An Interactive Model of Teaching. 222-229
Session 5
- Jeong Han Kim, James R. Roche: On the Optimal Capacity of Binary Neural Networks: Rigorous Combinatorial Approaches. 240-249
- Norbert Klasner, Hans-Ulrich Simon: From Noise-Free to Noise-Tolerant and from On-line to Batch Learning. 250-257
- John Shawe-Taylor: Sample Sizes for Sigmoidal Neural Networks. 258-264
- Kim L. Blackmore, Robert C. Williamson, Iven M. Y. Mareels, William A. Sethares: Online Learning via Congregational Gradient Descent. 265-272
- Changfeng Wang, Santosh S. Venkatesh: Criteria for Specifying Machine Complexity in Learning. 273-280
- Jyrki Kivinen, Manfred K. Warmuth: The Perceptron Algorithm vs. Winnow: Linear vs. Logarithmic Mistake Bounds when few Input Variables are Relevant. 289-296
Session 6
- Jonathan Baxter: Learning Internal Representations. 311-320
- Baruch Awerbuch, Margrit Betke, Ronald L. Rivest, Mona Singh: Piecemeal Graph Exploration by a Mobile Robot (Extended Abstract). 321-328
- Paul Fischer: More or Less Efficient Agnostic Learning of Convex Polygons. 337-344
- Nader H. Bshouty, Sally A. Goldman, H. David Mathias: Noise-Tolerant Parallel Learning of Geometric Concepts. 345-352
- Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson: On Efficient Agnostic Learning of Linear Combinations of Basis Functions. 369-376
Session 7
- Peter L. Bartlett, Philip M. Long: More Theorems about Scale-sensitive Dimensions and Learning. 392-401
- David Haussler, Manfred Opper: General Bounds on the Mutual Information Between a Parameter and n Conditionally Independent Observations. 402-411
- Joel Ratsaby, Santosh S. Venkatesh: Learning from a Mixture of Labeled and Unlabeled Examples with Parametric Side Information. 412-417
- Dan Boneh: Learning Using Group Representations (Extended Abstract). 418-426
Session 8
- Javed A. Aslam, Scott E. Decatur: Specification and Simulation of Statistical Query Algorithms for Efficiency and Noise Tolerance. 437-446
- Nader H. Bshouty: Simple Learning Algorithms Using Divide and Conquer. 447-453
Corrigendum
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