Machine Learning: A Guide to Current Research (The Springer International Series in Engineering and Computer Science, 12)

ISBN 10: 1461294061 / ISBN 13: 9781461294061

Published by Springer, 2011

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Seller Inventory # ERICA77314612940616

Bibliographic Details

• Title: Machine Learning: A Guide to Current Research (The Springer International Series in Engineering and Computer Science, 12)
• Publisher: Springer
• Publication Date: 2011
• Language: English
• ISBN 10: 1461294061
• ISBN 13: 9781461294061
• Binding: Paperback
• Condition: Like New
• Book Type: book

About this title

Synopsis

One of the currently most active research areas within Artificial Intelligence is the field of Machine Learning. which involves the study and development of computational models of learning processes. A major goal of research in this field is to build computers capable of improving their performance with practice and of acquiring knowledge on their own. The intent of this book is to provide a snapshot of this field through a broad. representative set of easily assimilated short papers. As such. this book is intended to complement the two volumes of Machine Learning: An Artificial Intelligence Approach (Morgan-Kaufman Publishers). which provide a smaller number of in-depth research papers. Each of the 77 papers in the present book summarizes a current research effort. and provides references to longer expositions appearing elsewhere. These papers cover a broad range of topics. including research on analogy. conceptual clustering. explanation-based generalization. incremental learning. inductive inference. learning apprentice systems. machine discovery. theoretical models of learning. and applications of machine learning methods. A subject index IS provided to assist in locating research related to specific topics. The majority of these papers were collected from the participants at the Third International Machine Learning Workshop. held June 24-26. 1985 at Skytop Lodge. Skytop. Pennsylvania. While the list of research projects covered is not exhaustive. we believe that it provides a representative sampling of the best ongoing work in the field. and a unique perspective on where the field is and where it is headed.