Metalearning: Applications to Data Mining by Pavel BrazdilMetalearning: Applications to Data Mining by Pavel Brazdil

Metalearning: Applications to Data Mining

byPavel Brazdil

Paperback | November 22, 2010

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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.
Title:Metalearning: Applications to Data MiningFormat:PaperbackDimensions:176 pages, 23.5 × 15.5 × 0.17 inPublished:November 22, 2010Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3642092314

ISBN - 13:9783642092312

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Table of Contents

Metalearning - Concepts and Architectures.- Metalearning for Algorithm Recommendation.- Advanced Issues on Metalearning for Algorithm Recommendation.- Combining Base Learners.- Extending Metalearning to Data Mining and KDD.- Adaptive Learning.- Transfer of (Meta)knowledge Across Tasks.- Composition of Systems and Applications.- Lessons Learned and Future Work.

Editorial 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)