Principles and theory for data mining and machinelearning

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, dimension reduction, variable selection, and multiple comparisons. All these topics have undergone extraordinarily rapid development in recent years and this treatment offers a modern perspective emphasizing the most recent contributions. The presentation of foundational results is detailed and includes many accessible proofs not readily available outside original sources. While the orientation is conceptual and theoretical, the main points are regularly reinforced by computational comparisons. More theoretical book on the same subject as the book on statistical learning by Hastie/Tibshirani/Friedman INDICE: 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.

• ISBN: 978-0-387-98134-5
• Editorial: Springer
• Encuadernacion: Cartoné
• Páginas: 786
• Fecha Publicación: 01/08/2009
• Nº Volúmenes: 1
• Idioma: Inglés