Induction, Algorithmic Learning Theory, and Philosophy (Hardcover, 2007 ed.)

This is the first book to collect essays from philosophers, mathematicians and computer scientists working at the exciting interface of algorithmic learning theory and the epistemology of science and inductive inference. Readable, introductory essays provide engaging surveys of different, complementary, and mutually inspiring approaches to the topic, both from a philosophical and a mathematical viewpoint. Building upon this base, subsequent papers present novel extensions of algorithmic learning theory as well as bold, new applications to traditional issues in epistemology and the philosophy of science. The volume is vital reading for students and researchers seeking a fresh, truth-directed approach to the philosophy of science and induction, epistemology, logic, and statistics.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Series:	Logic, Epistemology, and the Unity of Science, 9
Release date:	August 2007
First published:	August 2007
Editors:	Michele Friend • Norma B. Goethe • Valentina S. Harizanov
Dimensions:	235 x 155 x 17mm (L x W x T)
Format:	Hardcover
Pages:	290
Edition:	2007 ed.
ISBN-13:	978-1-4020-6126-4
Categories:	Books > Humanities > Philosophy > Topics in philosophy > Epistemology, theory of knowledge Books > Science & Mathematics > Mathematics > Mathematical foundations > Mathematical logic Books > Philosophy > Topics in philosophy > Epistemology, theory of knowledge
LSN:	1-4020-6126-9
Barcode:	9781402061264

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