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Principles and Theory for Data Mining and Machine Learning
ISBN-13:
9780387981352
Veröffentl:
2009
Seiten:
786
Autor:
Bertrand Clarke
Serie:
Springer Series in Statistics
eBook Typ:
PDF
eBook Format:
EPUB
Kopierschutz:
1 - PDF Watermark
Sprache:
Englisch
Beschreibung:
This book is a thorough introduction to the most important topics in data mining and machine learning. It begins with a detailed review of classical function estimation and proceeds with chapters on nonlinear regression, classification, and ensemble methods. The final chapters focus on clustering, dimension reduction, variable selection, and multiple comparisons. All these topics have undergone extraordinarily rapid development in recent years and this treatment offers a modern perspective emphasizing the most recent contributions. The presentation of foundational results is detailed and includes many accessible proofs not readily available outside original sources. While the orientation is conceptual and theoretical, the main points are regularly reinforced by computational comparisons.
"Extensive treatment of the most up-to-date topicsProvides the theory and concepts behind popular and emerging methodsRange of topics drawn from Statistics, Computer Science, and Electrical Engineering"
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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