Learning from Good and Bad Data

Name: Learning from Good and Bad Data
Brand: Kluwer Academic Publishers
SKU: 9780898382631
Price: 891.68 RON
Availability: InStock

Autor: Philip D. Laird

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Editura: Kluwer Academic Publishers

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This monograph is a contribution to the study of the identification problem: the problem of identify...

Informații despre carte

Autor

Philip D. Laird

Limbă

engleză

Legare

Carte - Copertă tare

Pagini

212

EAN

9780898382631

ISBN

0898382637

Enbook ID

01401089

Editura

Kluwer Academic Publishers

Greutate

512

Dimensiuni

162 x 240 x 18

Descriere completă

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.

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Learning from Good and Bad Data

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