David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Minds and Machines 20 (2):291-301 (2010)
Forms of justification for inductive machine learning techniques are discussed and classified into four types. This is done with a view to introduce some of these techniques and their justificatory guarantees to the attention of philosophers, and to initiate a discussion as to whether they must be treated separately or rather can be viewed consistently from within a single framework.
|Keywords||Bayesian Guarantee Induction Kernel methods|
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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.
David Corfield, Bernhard Schölkopf & Vladimir Vapnik (2009). Falsificationism and Statistical Learning Theory: Comparing the Popper and Vapnik-Chervonenkis Dimensions. [REVIEW] Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 40 (1):51 - 58.
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