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
• Language: English
• Published: New York, NY : Springer New York, 2009.
• Series: Springer Series in Statistics,
• Subjects: Statistics.; Computer science.; Data mining.; Optical pattern recognition.; Bioinformatics.; Mathematical statistics.; Statistical Theory and Methods.; Data Mining and Knowledge Discovery.; Computational Biology/Bioinformatics.; Pattern Recognition.; Probability and Statistics in Computer Science.; Signal, Image and Speech Processing.
• Online Access: https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-0-387-98135-2

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, prediction
• Kernel smoothing
• Spline smoothing
• New wave nonparametrics
• Supervised learning: Partition methods
• Alternative nonparametrics
• Computational comparisons
• Unsupervised learning: Clustering
• Learning in high dimensions
• Variable selection
• Multiple testing.