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Principles and Theory for Data Mining and Machine Learning

Specificaties
Paperback, 786 blz. | Engels
Springer New York | 2009e druk, 2011
ISBN13: 9781461417071
Rubricering
Springer New York 2009e druk, 2011 9781461417071
Onderdeel van serie Springer Series in Statistics
Verwachte levertijd ongeveer 9 werkdagen

Samenvatting

Extensive treatment of the most up-to-date topics

Provides the theory and concepts behind popular and emerging methods

Range of topics drawn from Statistics, Computer Science, and Electrical Engineering

Specificaties

ISBN13:9781461417071
Taal:Engels
Bindwijze:paperback
Aantal pagina's:786
Uitgever:Springer New York
Druk:2009

Inhoudsopgave

Variability, Information, and Prediction.- Local Smoothers.- Spline Smoothing.- New Wave Nonparametrics.- Supervised Learning: Partition Methods.- Alternative Nonparametrics.- Computational Comparisons.- Unsupervised Learning: Clustering.- Learning in High Dimensions.- Variable Selection.- Multiple Testing.

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        Principles and Theory for Data Mining and Machine Learning