Articoli correlati a Learning from Good and Bad Data: 47

Learning from Good and Bad Data: 47 - Brossura

Laird, Philip D.

 
9781461289517: Learning from Good and Bad Data: 47
Brossura
ISBN 10: 1461289513 ISBN 13: 9781461289517
Casa editrice: Springer, 2011

Sinossi

This monograph is a contribution to the study of the identification problem: the problem of identifying an item from a known class us­ ing positive and negative examples. This problem is considered to be an important component of the process of inductive learning, and as such has been studied extensively. In the overview we shall explain the objectives of this work and its place in the overall fabric of learning research. Context. Learning occurs in many forms; the only form we are treat­ ing here is inductive learning, roughly characterized as the process of forming general concepts from specific examples. Computer Science has found three basic approaches to this problem: • Select a specific learning task, possibly part of a larger task, and construct a computer program to solve that task . • Study cognitive models of learning in humans and extrapolate from them general principles to explain learning behavior. Then construct machine programs to test and illustrate these models. xi Xll PREFACE • Formulate a mathematical theory to capture key features of the induction process. This work belongs to the third category. The various studies of learning utilize training examples (data) in different ways. The three principal ones are: • Similarity-based (or empirical) learning, in which a collection of examples is used to select an explanation from a class of possible rules.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Dalla quarta di copertina

This monograph is a contribution to the study of the identification problem: the problem of identifying an item from a known class us­ ing positive and negative examples. This problem is considered to be an important component of the process of inductive learning, and as such has been studied extensively. In the overview we shall explain the objectives of this work and its place in the overall fabric of learning research. Context. Learning occurs in many forms; the only form we are treat­ ing here is inductive learning, roughly characterized as the process of forming general concepts from specific examples. Computer Science has found three basic approaches to this problem: ¿ Select a specific learning task, possibly part of a larger task, and construct a computer program to solve that task . ¿ Study cognitive models of learning in humans and extrapolate from them general principles to explain learning behavior. Then construct machine programs to test and illustrate these models. xi Xll PREFACE ¿ Formulate a mathematical theory to capture key features of the induction process. This work belongs to the third category. The various studies of learning utilize training examples (data) in different ways. The three principal ones are: ¿ Similarity-based (or empirical) learning, in which a collection of examples is used to select an explanation from a class of possible rules.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Editore
Springer
Data di pubblicazione
2011
Lingua
Inglese
ISBN 10 
1461289513
ISBN 13 
9781461289517
Rilegatura
Copertina flessibile
Numero di pagine
212
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