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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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Clarke, Bertrand (Autor), Fokoue, Ernest (Autor), Zhang, Hao Helen (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York, NY : Springer New York : Imprint: Springer, 2009.
Edición:1st ed. 2009.
Colección:Springer Series in Statistics,
Temas:
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.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • 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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