Computational Models of Learning

Computational Models of Learning

Pub. Date:
Springer-Verlag New York, LLC


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Computational Models of Learning

In recent years, machine learning has emerged as a significant area of research in artificial intelligence and cognitive science. At present, research in the field is being intensified from both the point of view of theory and of implementation, and the results are being introduced in practice. Machine learning has recently become the subject of interest of many young and talented scientists whose bold ideas have greatly contributed to the broadening of knowledge in this rapidly developing field of science. This situation has manifested itself in an increasing number of valuable contributions to scientific journals. However, such papers are necessarily compact descriptions of research problems. Computational Models of Learning supplements these contributions and is a collection of more extensive essays. These essays provide the reader with an increased knowledge of carefully selected problems of machine learning.

Product Details

ISBN-13: 9780387163185
Publisher: Springer-Verlag New York, LLC
Publication date: 11/01/1987
Series: Symbolic Computation Series
Pages: 208
Product dimensions: 6.50(w) x 1.50(h) x 9.50(d)

Table of Contents

Learning Strategies and Automated Knowledge Acquisition: An Overview.- Heuristics for Empirical Discovery.- Transfer of Training in Procedural Learning: A Matter of Conjectures and Refutations?.- Conceptual Knowledge Acquisition in Search.- Cognitive Development as Optimisation.

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