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Introduction: Machine learning as philosophy of science
Minds and Machines 14 (4):433-440 (2004)
Abstract
I consider three aspects in which machine learning and philosophy of science can illuminate each other: methodology, inductive simplicity and theoretical terms. I examine the relations between the two subjects and conclude by claiming these relations to be very close.Author's Profile
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Citations of this work
Integrating Philosophy of Understanding with the Cognitive Sciences.Kareem Khalifa, Farhan Islam, J. P. Gamboa, Daniel Wilkenfeld & Daniel Kostić - 2022 - Frontiers in Systems Neuroscience 16.
Simple Models in Complex Worlds: Occam’s Razor and Statistical Learning Theory.Falco J. Bargagli Stoffi, Gustavo Cevolani & Giorgio Gnecco - 2022 - Minds and Machines 32 (1):13-42.
The Big Data razor.Ezequiel López-Rubio - 2020 - European Journal for Philosophy of Science 10 (2):1-20.
The evolving hierarchy of naturalized philosophy: A metaphilosophical sketch.Luca Rivelli - 2024 - Metaphilosophy 55 (3):285-300.
References found in this work
Logical foundations of probability.Rudolf Carnap - 1950 - Chicago]: Chicago University of Chicago Press.
Scientific reasoning: the Bayesian approach.Peter Urbach & Colin Howson - 1993 - Chicago: Open Court. Edited by Peter Urbach.
What Computers Still Can’T Do: A Critique of Artificial Reason.Hubert L. Dreyfus - 1992 - MIT Press.
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