Chapter: 1: The Probably Approximately Correct Learning Model

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Home > An Introduction to Computational Learning Theory > 1: The Probably Approximately Correct Learning Model

An Introduction to Computational Learning Theory |

Michael J. Kearns; Umesh Vazirani

MIT Press,

An Introduction to Computational Learning Theory

1: The Probably Approximately Correct Learning Model

Chapter Info

ARI Id

1671269587259_11309

Access

Not Available Free

DOI

10.7551/mitpress/3897.003.0002

Chapter URL

https://direct.mit.edu/books/book/2604/chapter/70321/The-Probably-Approximately-Correct-Learning-Model

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Chapters/Headings	Author(s)	Pages	Info
1: The Probably Approximately Correct Learning Model
2: Occam's Razor
3: The Vapnik-Chervonenkis Dimension
4: Weak and Strong Learning
5: Learning in the Presence of Noise
6: Inherent Unpredictability
7: Reducibility in PAC Learning
8: Learning Finite Automata by Experimentation
9: Appendix: Some Tools for Probabilistic Analysis
Bibliography
Index
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