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Metalearning Applications to Data Mining

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.

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  • "Metalearning: Concepts and Systems; Metalearning for Algorithm Recommendation: an Introduction; Development of Metalearning Systems for Algorithm Recommendation; Extending Metalearning to Data Mining and KDD; Combining Base-Learners; Bias Management in Time-Changing Data Streams; Transfer of Metaknowledge Across Tasks; Composition of Complex Systems: Role of Domaín-Specifíc Metaknowledge."
  • "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."
  • "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."@en

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  • "Electronic books"
  • "Electronic books"@en
  • "Online-Publikation"

http://schema.org/name

  • "Metalearning Applications to Data Mining"@en
  • "Metalearning Applications to Data Mining"
  • "Metalearning applications to data mining"
  • "Metalearning applications to data mining"@en
  • "Metalearning"
  • "Metalearning : applications to data mining : with 53 figures and 11 tables"
  • "Metalearning : applications to data mining"@en
  • "Metalearning : applications to data mining"
  • "Metalearning : applications to data mining ; with 11 tables"
  • "Metalearning : Applications to Data Mining"