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

Principles and Theory for Data Mining and Machine Learning (Springer Series in Statistics)
Item# 11011340077
Retail price: US$79.95
Sale price: US$7.00

all items in this store are to be sent to your email within 24 hours after cleared payment. PDF eBooks are sent to you as email attachments. as for mp3 audiobook, a download link from ONEDRIVE will be sent to your email for you to download.

1. This item is an E-Book in PDF format.

2. Shipping & Delivery: Send to you by E-mail within 24 Hours after cleared payment. Immediately Arrival!!!

3. Shipping ( by email) + Handling Fee = US$0.00

4. Time-Limited Offer, Order Fast.

*************************************************************************





Principles and Theory for Data Mining and Machine Learning

(Springer Series in Statistics)

by Bertrand Clarke (Author), Ernest Fokoue (Author), Hao Helen Zhang (Author)



Publisher: Springer; 1 edition (July 30, 2009)





Statistics > Statistical Theory and Methods





Product Description

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 can be carried out readily. The book also serves as a handbook for researchers who want a conceptual overview of the central topics in data mining and machine learning.