Falsificationism and statistical learning theory: Comparing the Popper and vapnik-chervonenkis dimensions [Book Review]
David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Journal for General Philosophy of Science 40 (1):51 - 58 (2009)
We compare Karl Popper’s ideas concerning the falsifiability of a theory with similar notions from the part of statistical learning theory known as VC-theory . Popper’s notion of the dimension of a theory is contrasted with the apparently very similar VC-dimension. Having located some divergences, we discuss how best to view Popper’s work from the perspective of statistical learning theory, either as a precursor or as aiming to capture a different learning activity.
|Keywords||Induction Popper Statistical learning theory Falsification Dimension of a theory|
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References found in this work BETA
Gilbert Harman & Sanjeev Kulkarni (2007). Reliable Reasoning: Induction and Statistical Learning Theory. A Bradford Book.
Pavel Tichý (1974). On Popper's Definitions of Verisimilitude. British Journal for the Philosophy of Science 25 (2):155-160.
Peter Turney (1991). A Note on Popper's Equation of Simplicity with Falsifiability. British Journal for the Philosophy of Science 42 (1):105-109.
Citations of this work BETA
David Corfield (2010). Varieties of Justification in Machine Learning. Minds and Machines 20 (2):291-301.
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