Concept Formation: Knowledge and Experience in Unsupervised Learning

Concept Formation: Knowledge and Experience in Unsupervised Learning

by Douglas H. Fisher, Michael J. Pazzani
     
 

Concept formation lies at the center of learning and cognition. Unlike much work in machine learning and cognitive psychology, research on this topic focuses on the unsupervised and incremental acquisition of conceptual knowledge. Recent work on concept formation addresses a number of important issues. Foremost among these are the principles of similarity that

Overview

Concept formation lies at the center of learning and cognition. Unlike much work in machine learning and cognitive psychology, research on this topic focuses on the unsupervised and incremental acquisition of conceptual knowledge. Recent work on concept formation addresses a number of important issues. Foremost among these are the principles of similarity that guide concept learning and retrieval in human and machine, including the contribution of surface features, goals, and `deep' features. Another active area of research explores mechanisms for efficiently reorganizing memory in response to the ongoing experiences that confront intelligent agents. Finally, methods for concept formation play an increasing role in work on problem solving and planning, developmental psychology, engineering applications, and constructive induction.

This book brings together results on concept formation from cognitive psychology and machine learning, including explanation-based and inductive approaches. Chapters from these differing perspectives are intermingled to highlight the commonality of their research agendas. In addition to cognitive scientists and AI researchers, the book will interest data analysts involved in clustering, philosophers concerned with the nature and origin of concepts, and any researcher dealing with issues of similarity, memory organization, and problem solving.

Editorial Reviews

Booknews
Recent work in concept formation addresses a number of issues important not only to cognitive scientists and AI researchers, but also to data analysts involved in clustering, philosophers concerned with the nature and origin of concepts, and any researcher dealing with issues of similarity, memory organization, and problem solving. Chapters from the perspectives of cognitive psychology and machine learning are intermingled to highlight the commonality of their research agendas. Annotation c. Book News, Inc., Portland, OR (booknews.com)

Product Details

ISBN-13:
9781558602014
Publisher:
Elsevier Science & Technology Books
Publication date:
07/01/1991
Series:
Machine Learning Series
Pages:
488
Product dimensions:
7.44(w) x 9.29(h) x 1.03(d)

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