Abstract
As they say, nothing is more practical than a good theory. And indeed, mathematical models of learnability have helped improve our understanding of what it takes to induce a useful classifier from data, and, conversely, why the outcome of a machine-learning undertaking so often disappoints. And so, even though this textbook does not want to be mathematical, it cannot help introducing at least the basic concepts of the computational learning theory.
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Notes
- 1.
The reader will have noticed that all these requirements are satisfied by the “pies” domain from Chap. 1
- 2.
Recall that in the “pies” domain from Chap. 1, the size of the hypothesis space was | H | = 2108. Of these hypotheses, 296 classified correctly the entire training set.
- 3.
For instance, a variation of the hill-climbing search from Sect. 1.2 might be used to this end.
- 4.
Analysis of situations where these requirements are not satisfied would go beyond the scope of an introductory textbook.
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Kubat, M. (2017). Computational Learning Theory. In: An Introduction to Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-319-63913-0_7
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