Reliable Reasoning (Jean Nicod Lectures): Induction and Statistical Learning Theory - Brossura

Harman, Gilbert

Brossura

ISBN 10: 0262517345 ISBN 13: 9780262517348

Casa editrice: MIT Press, 2012

Riassunto

The implications for philosophy and cognitive science of developments in statistical learning theory.

In Reliable Reasoning, Gilbert Harman and Sanjeev Kulkarni—a philosopher and an engineer—argue that philosophy and cognitive science can benefit from statistical 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 inductive reasoning, where the reliability of a method is measured by its statistically expected percentage of errors—a central topic in SLT.

After discussing philosophical attempts to evade the problem of induction, Harman and Kulkarni provide an admirably clear account of the basic framework 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 authors discuss various topics in machine learning, including nearest-neighbor methods, neural networks, and support vector machines. Finally, they describe transductive reasoning and suggest possible new models of human reasoning suggested by developments in SLT.

Su questo libro

L'autore:

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.

Su questa edizione

• Editore: MIT Press
• Data di pubblicazione: 2012
• ISBN 10: 0262517345
• ISBN 13: 9780262517348
• Rilegatura: Copertina flessibile
• Numero di pagine: 118