Reliable Reasoning ― Induction and Statistical Learning Theory
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In Reliable Reasoning, Gilbert Harman and Sanjeev Kulkarni -- aphilosopher and an engineer -- argue that philosophy and cognitive science can benefit fromstatistical learning theory (SLT), the theory that lies behind recent advances in machine learning.The philosophical problem of induction, for example, is in part about the reliability of inductivereasoning, where the reliability of a method is measured by its statistically expected percentage oferrors -- a central topic in SLT.
After discussing philosophical attempts to evadethe problem of induction, Harman and Kulkarni provide an admirably clear account of the basicframework of SLT and its implications for inductive reasoning. They explain the Vapnik-Chervonenkis(VC) dimension of a set of hypotheses and distinguish two kinds of inductive reasoning. The authorsdiscuss various topics in machine learning, including nearest-neighbor methods, neural networks, andsupport vector machines. Finally, they describe transductive reasoning and suggest possible newmodels of human reasoning suggested by developments in SLT.
作者簡介
Gilbert Harman and Sanjeev Kulkarni are coauthors of An Elementary Introduction to Statistical Learning Theory. Harman is James S. McDonnell Distinguished University Professor of Philosophy at Princeton. Kulkarni is Professor of Electrical Engineering, an associated member of the Department of Philosophy, and Master of Butler College at Princeton University.
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