Abstract
In this chapter I discuss connections between machine learning and the philosophy of science. First I consider the relationship between the two disciplines. There is a clear analogy between hypothesis choice in science and model selection in machine learning. While this analogy has been invoked to argue that the two disciplines are essentially doing the same thing and should merge, I maintain that the disciplines are distinct but related and that there is a dynamic interaction operating between the two: a series of mutually beneficial interactions that changes over time. I will introduce some particularly fruitful interactions, in particular the consequences of automated scientific discovery for the debate on inductivism versus falsificationism in the philosophy of science, and the importance of philosophical work on Bayesian epistemology and causality for contemporary machine learning. I will close by suggesting the locus of a possible future interaction: evidence integration.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Williamson, J. (2009). The Philosophy of Science and its relation to Machine Learning. In: Gaber, M. (eds) Scientific Data Mining and Knowledge Discovery. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02788-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-02788-8_4
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02787-1
Online ISBN: 978-3-642-02788-8
eBook Packages: Computer ScienceComputer Science (R0)