Proceedings of the Third Annual Workshop on Computational Learning Theory University of Rochester, Rochester, New York, August 6-8, 1990
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| Corporate Authors: | , , |
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| Format: | eBook |
| Language: | English |
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San Mateo, Calif.
Morgan Kaufmann Publishers
1990
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| Collection: | O'Reilly - Collection details see MPG.ReNa |
Table of Contents:
- 2 Stochastic Rules and Their Hierarchical Parameter Structures3 A Learning Criterion for Stochastic Rules
- A Stochastic PAC Model; 4 Hierarchical Learning Based on the MDL Principle; 5 The Optimality of MDL Rules and Their Convergence Rates; 6 Sample Complexity and Learnability of Stochastic Decision List Classes; 7 Concluding Remarks; References; Chapter 6. ON THE COMPLEXITY OF LEARNING MINIMUM TIME-BOUNDED TURING MACHINES; Abstract; 1. INTRODUCTION; 2. DEFINITIONS; 3. MAIN RESULTS; 4. PROOFS; 5. OPEN QUESTIONS; References; Chapter 7. INDUCTIVE INFERENCE FROM POSITIVE DATA IS POWERFUL
- Includes bibliographical references and index
- ABSTRACTINTRODUCTION; PRELIMINARIES; ELEMENTARY FORMAL SYSTEMS; INDUCTIVE INFERENCE FROM POSITIVE DATA; INDUCTIVE INFERENCE OF EFS MODELS FROM POSITIVE DATA; INDUCTIVE INFERENCE OF EFS LANGUAGES FROM POSITIVE DATA; DISCUSSION; Acknowledgments; References; Chapter 8. INDUCTIVE IDENTIFICATION OF PATTERN LANGUAGES WITH RESTRICTED SUBSTITUTIONS; ABSTRACT; PATTERN LANGUAGES OVER AN ARBITRARY BASE; PUMPING LEMMA; APPLICATION TO INDUCTIVE INFERENCE; References; Chapter 9. Pattern Languages Are Not Learnable; 1 Introduction; 2 PRELIMINAR IES; 3 The Main Result; Acknowledgments; References