Principles and Theory for Data Mining and Machine Learning

• Main Authors: Clarke, Bertrand (Author), Fokoue, Ernest (Author), Zhang, Hao Helen (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: New York, NY : Springer New York, 2009.
• Series: Springer Series in Statistics,
• Subjects: Computer science.; Data mining.; Artificial intelligence.; Pattern recognition.; Bioinformatics.; Probabilities.; Statistics.; Computer Science.; Data Mining and Knowledge Discovery.; Artificial Intelligence (incl. Robotics).; Probability Theory and Stochastic Processes.; Statistical Theory and Methods.; Computational Biology/Bioinformatics.; Pattern Recognition.

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, d...

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.