Abstract
This chapter provides an analysis of the relationship of the traditional problems of justifying inductive inferences to the field of machine learning. After the summary of the philosophical problems of induction, text focus on the two philosophical problems relevant to the supervised and unsupervised machine learning. The former is a famous new riddle of induction devised by N. Goodman. The author argues that remarkable results in the theory of machine learning, no-free-lunch theorems are a formalisation of this traditional philosophical problem. Consequently, lengthy philosophical discussions on this problem are relevant to these results and vice versa. The later problem is the problem of similarity, as identified by N. Goodman and W. V. Quine. It is claimed that those discussions can help practitioners of unsupervised learning to be aware of its limitations.
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Notes
- 1.
This part of the text is an extended version of the author’s text “How Gruesome are the No-free-lunch Theorems for Machine Learning?” [16].
- 2.
We suppose that the new riddle is a different issue from the classical problem of induction, what is the received position with a few notable exception.
- 3.
Rodriguez-Pereyra claims there are strong philosophical grounds that Leibniz thought that the similarity of substances does not derive from the similarity of their properties (accidents) [24].
- 4.
Although there are readings of Hume that suggest that Hume’s position on similarity is closer to Goodman’s view [7].
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Lauc, D. (2020). Machine Learning and the Philosophical Problems of Induction. In: Skansi, S. (eds) Guide to Deep Learning Basics. Springer, Cham. https://doi.org/10.1007/978-3-030-37591-1_9
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