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130626  eng 
020 


a 9781402061271

100 
1 

a Friend, Michèle
e [editor]

245 
0 
0 
a Induction, Algorithmic Learning Theory, and Philosophy
h Elektronische Ressource
c edited by Michèle Friend, Norma B. Goethe, Valentina S. Harizanov

250 


a 1st ed. 2007

260 


a Dordrecht
b Springer Netherlands
c 2007, 2007

300 


a XIV, 290 p
b online resource

505 
0 

a to the Philosophy and Mathematics of Algorithmic Learning Theory  to the Philosophy and Mathematics of Algorithmic Learning Theory  Technical Papers  Inductive Inference Systems for Learning Classes of Algorithmically Generated Sets and Structures  Deduction, Induction, and beyond in Parametric Logic  How Simplicity Helps You Find the Truth without Pointing at it  Induction over the Continuum  Philosophy Papers  Logically Reliable Inductive Inference  Some Philosophical Concerns about the Confidence in ‘Confident Learning’  How to Do Things with an Infinite Regress  TradeOffs  Two Ways of Thinking about Induction  Between History and Logic

653 


a Mathematical logic

653 


a Algorithms

653 


a Formal Languages and Automata Theory

653 


a Cognitive Psychology

653 


a Cognitive psychology

653 


a Machine theory

653 


a Knowledge, Theory of

653 


a Science / Philosophy

653 


a Epistemology

653 


a Mathematical Logic and Foundations

653 


a Philosophy of Science

700 
1 

a Goethe, Norma B.
e [editor]

700 
1 

a Harizanov, Valentina S.
e [editor]

041 
0 
7 
a eng
2 ISO 6392

989 


b Springer
a Springer eBooks 2005

490 
0 

a Logic, Epistemology, and the Unity of Science

028 
5 
0 
a 10.1007/9781402061271

856 
4 
0 
u https://doi.org/10.1007/9781402061271?nosfx=y
x Verlag
3 Volltext

082 
0 

a 120

520 


a 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, truthdirected approach to the philosophy of science and induction, epistemology, logic, and statistics
