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.

List 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.

Product details

• Assisted by: Leonar Bolc (Editor), Leonard Bolc (Editor)
• Publisher: Springer, Berlin
• Languages: English
• Product format: Paperback / Softback
• Released: 05.12.2012
• EAN: 9783642827440
• ISBN: 978-3-642-82744-0
• No. of pages: 208
• Illustrations: IX, 208 p.
• Series: Artificial Intelligence; Symbolic computation/Artificial Intelligence; Symbolic Computation; Artificial Intelligence; Symbolic Computation
• Subject: Natural sciences, medicine, IT, technology > IT, data processing > IT