Statistical learning theory as a framework for the philosophy of induction
| Abstract | Statistical Learning Theory (e.g., Hastie et al., 2001; Vapnik, 1998, 2000, 2006) is the basic theory behind contemporary machine learning and data-mining. We suggest that the theory provides an excellent framework for philosophical thinking about inductive inference. | |||||||||
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Similar books and articlesOliver Schulte, Formal Learning Theory. Stanford Encyclopedia of Philosophy.
S. Russell (1991). Inductive Learning by Machines. Philosophical Studies 64 (October):37-64.
Kevin T. Kelly, Oliver Schulte & Cory Juhl (1997). Learning Theory and the Philosophy of Science. Philosophy of Science 64 (2):245-267.
Fei Xu & Joshua B. Tenenbaum (2001). Rational Statistical Inference: A Critical Component for Word Learning. Behavioral and Brain Sciences 24 (6):1123-1124.
Kevin Kelly (2008). Review of Gilbert Harman, Sanjeev Kulkarni, Reliable Reasoning: Induction and Statistical Learning Theory. [REVIEW] Notre Dame Philosophical Reviews 2008 (3).
Daniel Steel (2009). Testability and Ockham's Razor: How Formal and Statistical Learning Theory Converge in the New Riddle of Induction. Journal of Philosophical Logic 38 (5):471 - 489.
David Corfield, Bernhard Schölkopf & Vladimir Vapnik (2009). Falsificationism and Statistical Learning Theory: Comparing the Popper and Vapnik-Chervonenkis Dimensions. Journal for General Philosophy of Science 40 (1):51 - 58.
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