The Elements of Statistical Learning: Data Mining, Inference, and Prediction.
This book descibes the important ideas of data mining, machine learning, and bioinformatics in a common conceptual framework. Topics include neural networks, support vector machines, classification trees and boosting.
Tag(s): Data Mining Machine Learning Statistics
Publication date: 01 Dec 2015
ISBN-10: 0387848576
ISBN-13: 978-038784857
Paperback: 745 pages
Views: 9,745
The Elements of Statistical Learning: Data Mining, Inference, and Prediction.
About The Author(s)
Dr. Friedman is one of the world's leading researchers in statistics and data mining. He has been a Professor of Statistics at Stanford University for over 20 years and has published on a wide range of data mining topics including nearest neighbor classification, logistical regressions, and high-dimensional data analysis. His primary research interest is in the area of machine learning.
Dr. Friedman is one of the world's leading researchers in statistics and data mining. He has been a Professor of Statistics at Stanford University for over 20 years and has published on a wide range of data mining topics including nearest neighbor classification, logistical regressions, and high-dimensional data analysis. His primary research interest is in the area of machine learning.
Trevor Hastie is The John A. Overdeck Professor, Professor of Statistics, and Professor of Biomedical Data Science at Stanford University.
Trevor Hastie is The John A. Overdeck Professor, Professor of Statistics, and Professor of Biomedical Data Science at Stanford University.
Robert Tibshirani is a Professor of Health Research and Policy, and Statistics at Stanford University.
Robert Tibshirani is a Professor of Health Research and Policy, and Statistics at Stanford University.