Principles and Theory for Data Mining and Machine Learning

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...

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Bibliographic Details
Call Number:Libro Electrónico
Main Authors: Clarke, Bertrand (Author), Fokoue, Ernest (Author), Zhang, Hao Helen (Author)
Corporate Author: SpringerLink (Online service)
Format: Electronic eBook
Language:Inglés
Published: New York, NY : Springer New York : Imprint: Springer, 2009.
Edition:1st ed. 2009.
Series:Springer Series in Statistics,
Subjects:
Data mining.
Artificial intelligence.
Probabilities.
Statistics .
Bioinformatics.
Pattern recognition systems.
Data Mining and Knowledge Discovery.
Artificial Intelligence.
Probability Theory.
Statistical Theory and Methods.
Computational and Systems Biology.
Automated Pattern Recognition.
Online Access:Texto Completo
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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