Principles and Theory for Data Mining and Machine Learning

2009. Auflage 2009
ISBN: 978-0-387-98134-5
Verlag: Springer

Buch, Englisch, 786 Seiten, Format (B × H): 165 mm x 246 mm, Gewicht: 1242 g

Reihe: Springer Series in Statistics

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.

Zielgruppe

Research

Autoren/Hrsg.

Clarke, Bertrand

Fokoue, Ernest

Zhang, Hao Helen

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

Inhaltsverzeichnis

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.

Über Autor(innen)

Bertrand Clarke is a Professor of Statistics in the Department of Medicine, Department of Epidemiology and Public Health, and the Center for Computational Sciences at the University of Miami. He has been on the Editorial Board of the Journal of the American Statistical Association, the Journal of Statistical Planning and Inference, and Statistical Papers. He is co-winner, with Andrew Barron, of the 1990 Browder J. Thompson Prize from the Institute of Electrical and Electronic Engineers.

Ernest Fokoue is an Assistant Professor of Statistics at Kettering University. He has also taught at Ohio State University and been a long term visitor at the Statistical and Mathematical Sciences Institute where he was a Post-doctoral Research Fellow in the Data Mining and Machine Learning Program. In 2000, he was the winner of the Young Researcher Award from the International Association for Statistical Computing.

Hao Helen Zhang is an Associate Professor of Statistics in the Department of Statistics at North Carolina State University. For 2003-2004, she was a Research Fellow at SAMSI and in 2007, she won a Faculty Early Career Development Award from the National Science Foundation. She is on the Editorial Board of the Journal of the American Statistical Association and Biometrics.