Can I read Principles and Theory for Data Mining and Machine Learning (Springer Series in Statistics) on EtoBox?

Principles and Theory for Data Mining and Machine Learning (Springer Series in Statistics) by Bertrand Clarke, Ernest Fokoue, Hao Helen Zhang (auth.) is a nonfiction available to read on EtoBox.

What is Principles and Theory for Data Mining and Machine Learning (Springer Series in Statistics) about?

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. Intended primarily as a graduate level textbook for statistics, computer science, and electrical engineering students, this book assumes only a strong foundation in undergraduate statistics and mathematics, and facility with using R packages. The text has a wide variety of problems, many of an exploratory nature. There are numerous computed examples, complete with code, so that further computations

Who reads Principles and Theory for Data Mining and Machine Learning (Springer Series in Statistics)?

Author

Bertrand Clarke, Ernest Fokoue, Hao Helen Zhang (auth.)

Publisher

Springer-Verlag New York

Published

2009

Language

EN

ISBN

9780387981345

Category

nonfiction

Subjects

Mathematics, Science, Biology