Principles and Theory for Data Mining and Machine Learning - Bertrand Clarke, Ernest Fokoue, Hao Helen Zhang

Principles and Theory for Data Mining and Machine Learning

Buch | Softcover
786 Seiten
2011
Springer-Verlag New York Inc.
978-1-4614-1707-1 (ISBN)
299,59 inkl. MwSt
The idea for this book came from the time the authors spent at the Statistics and Applied Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina starting in fall 2003. The rst author was there for a total of two years, the rst year as a Duke/SAMSI Research Fellow. The second author was there for a year as a Post-Doctoral Scholar. The third author has the great fortune to be in RTP p- manently. SAMSI was – and remains – an incredibly rich intellectual environment with a general atmosphere of free-wheeling inquiry that cuts across established elds. SAMSI encourages creativity: It is the kind of place where researchers can be found at work in the small hours of the morning – computing, interpreting computations, and developing methodology. Visiting SAMSI is a unique and wonderful experience. The people most responsible for making SAMSI the great success it is include Jim Berger, Alan Karr, and Steve Marron. We would also like to express our gratitude to Dalene Stangl and all the others from Duke, UNC-Chapel Hill, and NC State, as well as to the visitors (short and long term) who were involved in the SAMSI programs. It was a magical time we remember with ongoing appreciation.

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.

Erscheint lt. Verlag 2.12.2011
Reihe/Serie Springer Series in Statistics
Zusatzinfo XII, 786 p.
Verlagsort New York, NY
Sprache englisch
Maße 155 x 235 mm
Themenwelt Informatik Datenbanken Data Warehouse / Data Mining
Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Mathematik / Informatik Mathematik Wahrscheinlichkeit / Kombinatorik
ISBN-10 1-4614-1707-4 / 1461417074
ISBN-13 978-1-4614-1707-1 / 9781461417071
Zustand Neuware
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