Elements of Statistical Learning

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Trevor Hastie, Robert Tibshirani, Jerome Friedman, "The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition"
ISBN: 0387848576 | 2009 | EPUB | 745 pages | 13 MB
The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd Edition) [Repost]

Trevor Hastie, Robert Tibshirani, Jerome Friedman - The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd Edition)
Published: 2011-04-12 | ISBN: 0387848576 | PDF + DJVU | 745 pages | 23 MB
The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd Edition) [Repost]

Trevor Hastie, ‎Robert Tibshirani, ‎Jerome H. Friedman - The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd Edition)
Published: 2011-12-23 | ISBN: 0387848576 | PDF | 745 pages | 8 MB

T. Hastie, R. Tibshirani, J. H. Friedman, "The Elements of Statistical Learning"  eBooks & eLearning

Posted by Alexpal at April 5, 2006
T. Hastie, R. Tibshirani, J. H. Friedman, "The Elements of Statistical Learning"

T. Hastie, R. Tibshirani, J. H. Friedman, "The Elements of Statistical Learning"
Springer | ISBN 0387952845 | 2003 Year | DjVu | 4 Mb | 552 Pages

The Nature of Statistical Learning Theory [Repost]  eBooks & eLearning

Posted by tanas.olesya at July 8, 2016
The Nature of Statistical Learning Theory  [Repost]

The Nature of Statistical Learning Theory by Vladimir Vapnik
English | 19 Nov. 1999 | ISBN: 0387987800 | 334 Pages | PDF | 10 MB

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization.
The Nature of Statistical Learning Theory by Vladimir Vapnik [Repost]

The Nature of Statistical Learning Theory (Information Science and Statistics) by Vladimir Vapnik
English | Nov 19, 1999 | ISBN: 0387987800 | 334 Pages | PDF | 10 MB

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data.
The Nature of Statistical Learning Theory by Vladimir Vapnik [Repost]

The Nature of Statistical Learning Theory (Information Science and Statistics) by Vladimir Vapnik
Springer; 2nd edition | November 19, 1999 | English | ISBN: 0387987800 | 334 pages | PDF | 10 MB

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics.
Elements of Statistical Mechanics: With an Introduction to Quantum Field Theory and Numerical Simulation (Repost)

Ivo Sachs, Siddhartha Sen, James Sexton, "Elements of Statistical Mechanics: With an Introduction to Quantum Field Theory and Numerical Simulation"
English | 2006-05-29 | ISBN: 0521841984 | 348 pages | PDF | 2.0 mb
Vladimir Vapnik, The Nature of Statistical Learning Theory (Repost)

Vladimir Vapnik, The Nature of Statistical Learning Theory
ISBN: 0387987800 | edition 1999 | PDF | 334 pages | 10 mb

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics.

Information Theory and Statistical Learning (repost)  

Posted by interes at Jan. 30, 2014
Information Theory and Statistical Learning (repost)

Information Theory and Statistical Learning by Frank Emmert-Streib, Matthias Dehmer
English | 2008-11-14 | ISBN: 0387848150 | PDF | 407 pages | 6,7 MB

Information Theory and Statistical Learning presents theoretical and practical results about information theoretic methods used in the context of statistical learning.
The book will present a comprehensive overview of the large range of different methods that have been developed in a multitude of contexts.