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Cost-sensitive machine learning /

In machine learning applications, practitioners must take into account the cost associated with the algorithm. These costs include: Cost of acquiring training dataCost of data annotation/labeling and cleaningComputational cost for model fitting, validation, and testingCost of collecting features/att...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Krishnapuram, Balaji, Yu, Shipeng, Rao, Bharat
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Boca Raton, FL : CRC Press, ©2012.
Colección:Chapman & Hall/CRC machine learning & pattern recognition series.
Temas:
Acceso en línea:Texto completo
Texto completo

MARC

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505 0 |a pt. 1. Theoretical underpinnings of cost-sensitive machine learning -- pt. 2. Cost-sensitive machine learning applications. 
520 |a In machine learning applications, practitioners must take into account the cost associated with the algorithm. These costs include: Cost of acquiring training dataCost of data annotation/labeling and cleaningComputational cost for model fitting, validation, and testingCost of collecting features/attributes for test dataCost of user feedback collectionCost of incorrect prediction/classificationCost-Sensitive Machine Learning is one of the first books to provide an overview of the current research efforts and problems in this area. It discusses real-world applications that incorporate the cost o. 
590 |a O'Reilly  |b O'Reilly Online Learning: Academic/Public Library Edition 
590 |a ProQuest Ebook Central  |b Ebook Central Academic Complete 
650 0 |a Machine learning  |x Cost effectiveness. 
650 6 |a Apprentissage automatique  |x Coût-efficacité. 
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650 7 |a COMPUTERS  |x Intelligence (AI) & Semantics.  |2 bisacsh 
650 7 |a Machine learning  |2 fast 
700 1 |a Krishnapuram, Balaji. 
700 1 |a Yu, Shipeng. 
700 1 |a Rao, Bharat. 
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