Metalearning / Cognitive Technologies (PDF)
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...
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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.
- Autoren: Pavel Brazdil , Christophe Giraud Carrier , Carlos Soares , Ricardo Vilalta
- 2008, 2009, 176 Seiten, Englisch
- Verlag: Springer-Verlag GmbH
- ISBN-10: 3540732632
- ISBN-13: 9783540732631
- Erscheinungsdatum: 18.11.2008
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
- Dateiformat: PDF
- Größe: 6.72 MB
- Mit Kopierschutz
- Vorlesefunktion
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)
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