Overview
- Includes supplementary material: sn.pub/extras
Part of the book series: Cognitive Technologies (COGTECH)
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Table of contents (8 chapters)
Keywords
About this book
Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience.
This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves.
The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence.
Reviews
From the reviews:
"There are many techniques available for machine learning from data … . the problem is: given a set of data, which of the learning systems should one use? The goal of this book is to initiate a study of this problem. … The mixture of detailed description and overview is well managed. The reader is able to see how the authors’ ideas and work fit into a larger framework. Graduate students looking for thesis topics should read this book." (J. P. E. Hodgson, ACM Computing Reviews, May, 2009)
Authors and Affiliations
Bibliographic Information
Book Title: Metalearning
Book Subtitle: Applications to Data Mining
Authors: Pavel Brazdil, Christophe Giraud-Carrier, Carlos Soares, Ricardo Vilalta
Series Title: Cognitive Technologies
DOI: https://doi.org/10.1007/978-3-540-73263-1
Publisher: Springer Berlin, Heidelberg
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2009
Hardcover ISBN: 978-3-540-73262-4Published: 26 November 2008
eBook ISBN: 978-3-540-73263-1Published: 18 November 2008
Series ISSN: 1611-2482
Series E-ISSN: 2197-6635
Edition Number: 1
Number of Pages: XI, 176
Number of Illustrations: 53 b/w illustrations
Topics: Artificial Intelligence, Data Mining and Knowledge Discovery, Pattern Recognition