Description 
1 online resource 
Contents 
Front Cover; Proceedings of the Second Annual Workshop on Computational Learning Theory; Copyright Page; Table of Contents; Foreword; Part 1: Invited Lecture; Chapter 1. Inductive Principles of the Search for Empirical Dependences; 1. Introduction; 2. The problem of expected risk minimization; 3. The principle of empirical risk minimization; 4. The concept of consistency and strong consistency; 5. Strong consistency and uniform convergence; 6. Necessary and sufficient conditions of uniform convergence; 7. The relation to the theory of falsifiability by K. Popper 

1 Introduction2 Approximate Truth; 3 Some deductive logic of approximate truth; 4 Some inductive logic of approximate truth; 5 Stable predicates; 6 Concluding remarks; 7 References; Chapter 7. Informed parsimonious inference of prototypical genetic sequences; Abstract; 1 Introduction; 2 Model of sequence generation; 3 Bayes model; 4 Expressing the inductive assumptions; 5 Computing the optimal theory; 6 Experimental results; 7 Comparison to the biological parsimony methods; 8 Acknowledgements; References; Chapter 8. COMPLEXITY ISSUES IN LEARNING BY NEURAL NETS; Abstract; 1 INTRODUCTION 

2 DEFINITIONS3 NEURAL NET DESIGN PROBLEMS; 4 TRAINING NEURAL NETS; 5 CASCADE NEURAL NETS; 6 CONCLUSIONS; REFERENCES; Chapter 9. Equivalence Queries and Approximate Fingerprints; Abstract; 1 Introduction; 2 The basic idea; 3 Representations of concepts; 4 Some examples of approximate fingerprints; 5 Comments; 6 Acknowledgments; References; Chapter 10. LEARNING READONCE FORMULAS USING MEMBERSHIP QUERIES; ABSTRACT; 1. INTRODUCTION; 2. LEARNING EQUIVALENT READONCE FORMULAS FROM EXPLICITLY GIVEN FORMULAS; 3. PRELIMINARIES; 4. LEARNING READONCE FORMULAS WITH A RELEVANT POSSIBILITY ORACLE 

3 Conclusions and Open Problems4 Acknowledgements; References; Chapter 4. A Polynomialtime Algorithm for Learningfcvariable Pattern Languages from Examples; 1 Introduction; 2 Definitions and Notation; 3 The Algorithm COVER; 4 Good Things and Bad Things; 5 The Event Tree; 6 Putting it All Together; 7 Conclusions and Future Research; References; Chapter 5. ON LEARNING FROM EXERCISES; ABSTRACT; 1. INTRODUCTION; 2. LEARNING FROM SOLVED INSTANCES; 3. AN APPLICATION; 4. LEARNING FROM EXERCISES; 5. CONCLUSION; Acknowledgements; References; Appendix A; Chapter 6. On Approximate Truth; Abstract 

8. The capacity of a set of functions9. Theorems about the rate of uniform convergence; 10. The principle of structural risk minimization; 11. Concluding remarks; REFERENCES; Part 2: Technical Papers; Chapter 2. Polynomial Learnability of Semilinear Sets; Abstract; 1 Introduction; 2 Results and Significance; 3 Learnability Models Used; 4 Classes of Concepts Considered; 5 Technical Details; 6 Open Problems; Acknowledgments; References; Chapter 3. LEARNING NESTED DIFFERENCES OF INTERSECTIONCLOSED CONCEPT CLASSES; ABSTRACT; 1 Introduction; 2 The InclusionExclusion Algorithms 
Notes 
International conference proceedings 
Bibliography 
Includes bibliographical references and index 
Notes 
Online resource; title from PDF title page (ScienceDirect, viewed November 17, 2014) 

Print version record 
Subject 
Machine learning  Congresses.

Genre/Form 
Conference papers and proceedings.


Conference papers and proceedings.

Form 
Electronic book

Author 
Haussler, David, editor


Rivest, Ronald L., editor


Warmuth, Manfred, editor

ISBN 
0080948294 (electronic bk.) 

9780080948294 (electronic bk.) 
