Reliable Reasoning: Induction and Statistical Learning Theory - Rilegato

Harman, Gilbert; Kulkarni, Sanjeev

Rilegato

ISBN 10: 0262083604 ISBN 13: 9780262083607

Casa editrice: Bradford Books, 2007

Riassunto

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:

Sanjeev Kulkarni is Professor of Electrical Engineering and an associated faculty member of the Department of Philosophy at Princeton University.

Product Description:

Book by Harman Gilbert Kulkarni Sanjeev

Su questa edizione

• Editore: Bradford Books
• Data di pubblicazione: 2007
• ISBN 10: 0262083604
• ISBN 13: 9780262083607
• Rilegatura: Copertina rigida
• Numero edizione: 1
• Numero di pagine: 108