Editors:
- Only book to combine philosophy essays with technical essays on algorithmic learning theory
- Invaluable for the reflective computer scientist or the mathematician/logician interested in modelling learning
- Deepens the argument and debate about mathematically modelling learning
- No-one with a serious interest in the philosophy of science can afford to ignore this development
- Refracts the classical problem of induction into a spectrum of new insights and questions
Part of the book series: Logic, Epistemology, and the Unity of Science (LEUS, volume 9)
Buy it now
Buying options
Tax calculation will be finalised at checkout
Other ways to access
This is a preview of subscription content, log in via an institution to check for access.
Table of contents (11 chapters)
-
Front Matter
-
Introduction to the Philosophy and Mathematics of Algorithmic Learning Theory
-
Front Matter
-
-
Back Matter
About this book
Editors and Affiliations
-
George Washington University, Washington, U.S.A
Michèle Friend, Valentina S. Harizanov
-
National University of Cordoba, Cordoba, Argentina
Norma B. Goethe
Bibliographic Information
Book Title: Induction, Algorithmic Learning Theory, and Philosophy
Editors: Michèle Friend, Norma B. Goethe, Valentina S. Harizanov
Series Title: Logic, Epistemology, and the Unity of Science
DOI: https://doi.org/10.1007/978-1-4020-6127-1
Publisher: Springer Dordrecht
eBook Packages: Humanities, Social Sciences and Law, Philosophy and Religion (R0)
Copyright Information: Springer Science+Business Media B.V. 2007
Hardcover ISBN: 978-1-4020-6126-4Published: 28 August 2007
Softcover ISBN: 978-90-481-7544-4Published: 25 November 2010
eBook ISBN: 978-1-4020-6127-1Published: 21 August 2007
Series ISSN: 2214-9775
Series E-ISSN: 2214-9783
Edition Number: 1
Number of Pages: XIV, 290
Topics: Epistemology, Mathematical Logic and Foundations, Mathematical Logic and Formal Languages, Philosophy of Science, Algorithms, Cognitive Psychology