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Principles and Theory for Data Mining and Machine Learning

Intended primarily as a graduate level textbook for statistics, computer science, and electrical engineering students, this book assumes only a strong foundation in undergraduate statistics and mathematics, and facility with using R packages. The text has a wide variety of problems, many of an explo...

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Bibliographic Details
Main Authors: Clarke, Bertrand, Fokoue, Ernest (Author), Zhang, Hao Helen (Author)
Format: eBook
Language:English
Published: New York, NY Springer New York 2009, 2009
Edition:1st ed. 2009
Series:Springer Series in Statistics
Subjects:
Statistical Theory And Methods
Bioinformatics
Computational And Systems Biology
Statistics 
Artificial Intelligence
Data Mining
Probability Theory
Data Mining And Knowledge Discovery
Automated Pattern Recognition
Pattern Recognition Systems
Probabilities
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Table of Contents:
  • 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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