Machine Learning - 1st Edition - ISBN: 9781558601482, 9780080510538

Machine Learning

1st Edition

A Theoretical Approach

Authors: Balas Natarajan
Hardcover ISBN: 9781558601482
eBook ISBN: 9780080510538
Imprint: Morgan Kaufmann
Published Date: 1st July 1991
Page Count: 217
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Description

Machine Learning: A Theoretical Approach
Balas K. Natarajan

  • Chapter 1 Introduction
    • 1.1 Bibliographic Notes
  • Chapter 2 Learning Concept on Countable Domains
    • 2.1 Preliminaries
    • 2.2 Sample Complexity
    • 2.3 Dimension and Learnability
    • 2.4 Learning Concepts with One-Sided Error
    • 2.5 Summary
    • 2.6 Appendix
    • 2.7 Exercises
    • 2.8 Bibliographic Notes
  • Chapter 3 Time Complexity of Concept Learning
    • 3.1 Preliminaries
    • 3.2 Polynomial-Time Learnability
    • 3.3 Occam's Razor
    • 3.4 One-Sided Error
    • 3.5 Hardness Results
    • 3.6 Summary
    • 3.7 Appendix
      • 3.7.1 Randomized Algorithms
      • 3.7.2 Chabyshev's Inequality
    • 3.8 Exercises
    • 3.9 Bibliographic Notes
  • Chapter 4 Learning Concepts on Uncoutable Domains
    • 4.1 Preliminaries
    • 4.2 Uniform Convergence and Learnability
    • 4.3 Summary
    • 4.4 Appendix
      • 4.4.1 Measurability and Probability Distributions
      • 4.4.2 Bounds for the Binomial Distribution
    • 4.5 Exercises
  • Chapter 5 Learning Functions
    • 5.1 Learning Functions on Countable Domains
      • 5.1.1 Dimension and Learnability
      • 5.1.2 Time Complexity of Function Learning
    • 5.2 Learning Functions on Uncountable Domains
    • 5.3 Summary
    • 5.4 Exercises
    • 5.5 Bibliographic Notes
  • Chapter 6 Finite Automata
    • 6.1 Preliminaries
    • 6.2 A Modified Framework
    • 6.3 Summary
    • 6.4 Exercises
    • 6.5 Bibliographic Notes
  • Chapter 7 Neural Networks
    • 7.1 Preliminaries
    • 7.2 Bounded-Precision Networks
    • 7.3 Efficiency Issues
    • 7.4 Summary
    • 7.5 Appendix
      • 7.5.1 Hyperplanes and Half-Spaces
    • 7.6 Exercises
    • 7.7 Bibliographic Notes
  • Chapter 8 Generalizing the Learning Model
    • 8.1 Preliminaries
    • 8.2 Sample Complexity
    • 8.3 Time Complexity
    • 8.4 Prediction
      • 8.4.1 Hardness Results
    • 8.5 Boosting
      • 8.5.1 Confidence Boosting
      • 8.5.2 Precision Boosting
    • 8.6 Summary
    • 8.7 Exercises
    • 8.8 Bibliographic Notes
  • Chapter 9 Conclusion
    • 9.1 The Paradigm
    • 9.2 Recent and Future Directions
    • 9.3 An AI Perspective
  • Index

Table of Contents

Machine Learning: A Theoretical Approach
Balas K. Natarajan

  • Chapter 1 Introduction
    • 1.1 Bibliographic Notes
  • Chapter 2 Learning Concept on Countable Domains
    • 2.1 Preliminaries
    • 2.2 Sample Complexity
    • 2.3 Dimension and Learnability
    • 2.4 Learning Concepts with One-Sided Error
    • 2.5 Summary
    • 2.6 Appendix
    • 2.7 Exercises
    • 2.8 Bibliographic Notes
  • Chapter 3 Time Complexity of Concept Learning
    • 3.1 Preliminaries
    • 3.2 Polynomial-Time Learnability
    • 3.3 Occam's Razor
    • 3.4 One-Sided Error
    • 3.5 Hardness Results
    • 3.6 Summary
    • 3.7 Appendix
      • 3.7.1 Randomized Algorithms
      • 3.7.2 Chabyshev's Inequality
    • 3.8 Exercises
    • 3.9 Bibliographic Notes
  • Chapter 4 Learning Concepts on Uncoutable Domains
    • 4.1 Preliminaries
    • 4.2 Uniform Convergence and Learnability
    • 4.3 Summary
    • 4.4 Appendix
      • 4.4.1 Measurability and Probability Distributions
      • 4.4.2 Bounds for the Binomial Distribution
    • 4.5 Exercises
  • Chapter 5 Learning Functions
    • 5.1 Learning Functions on Countable Domains
      • 5.1.1 Dimension and Learnability
      • 5.1.2 Time Complexity of Function Learning
    • 5.2 Learning Functions on Uncountable Domains
    • 5.3 Summary
    • 5.4 Exercises
    • 5.5 Bibliographic Notes
  • Chapter 6 Finite Automata
    • 6.1 Preliminaries
    • 6.2 A Modified Framework
    • 6.3 Summary
    • 6.4 Exercises
    • 6.5 Bibliographic Notes
  • Chapter 7 Neural Networks
    • 7.1 Preliminaries
    • 7.2 Bounded-Precision Networks
    • 7.3 Efficiency Issues
    • 7.4 Summary
    • 7.5 Appendix
      • 7.5.1 Hyperplanes and Half-Spaces
    • 7.6 Exercises
    • 7.7 Bibliographic Notes
  • Chapter 8 Generalizing the Learning Model
    • 8.1 Preliminaries
    • 8.2 Sample Complexity
    • 8.3 Time Complexity
    • 8.4 Prediction
      • 8.4.1 Hardness Results
    • 8.5 Boosting
      • 8.5.1 Confidence Boosting
      • 8.5.2 Precision Boosting
    • 8.6 Summary
    • 8.7 Exercises
    • 8.8 Bibliographic Notes
  • Chapter 9 Conclusion
    • 9.1 The Paradigm
    • 9.2 Recent and Future Directions
    • 9.3 An AI Perspective
  • Index

Details

No. of pages:
217
Language:
English
Copyright:
© Morgan Kaufmann 1991
Published:
Imprint:
Morgan Kaufmann
eBook ISBN:
9780080510538
Hardcover ISBN:
9781558601482

About the Author

Balas Natarajan