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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.
Acquisto del libro
Metalearning, Ricardo Vilalta, Pavel B. Brazdil, Carlos Soares, Christophe Giraud-Carrier
- Lingua
- Pubblicato
- 2010
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- (In brossura)
Metodi di pagamento
Qui potrebbe esserci la tua recensione.
- Titolo
- Metalearning
- Sottotitolo
- Applications to Data Mining
- Lingua
- Inglese
- Editore
- Springer
- Pubblicato
- 2010
- Formato
- In brossura
- Pagine
- 187
- ISBN10
- 3642092314
- ISBN13
- 9783642092312
- Serie
- Valutazione
- 4,5 su 5
- Descrizione
- 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.