Description: Please refer to the section BELOW (and NOT ABOVE) this line for the product details - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Title:The Elements Of Statistical Learning: Data Mining, Inference, And Prediction, Second EditionISBN13:9780387848570ISBN10:0387848576Author:Hastie, Trevor (Author), Tibshirani, Robert (Author), Friedman, Jerome (Author)Description:During The Past Decade There Has Been An Explosion In Computation And Information Technology With It Have Come Vast Amounts Of Data In A Variety Of Fields Such As Medicine, Biology, Finance, And Marketing The Challenge Of Understanding These Data Has Led To The Development Of New Tools In The Field Of Statistics, And Spawned New Areas Such As Data Mining, Machine Learning, And Bioinformatics Many Of These Tools Have Common Underpinnings But Are Often Expressed With Different Terminology This Book Describes The Important Ideas In These Areas In A Common Conceptual Framework While The Approach Is Statistical, The Emphasis Is On Concepts Rather Than Mathematics Many Examples Are Given, With A Liberal Use Of Color Graphics It Is A Valuable Resource For Statisticians And Anyone Interested In Data Mining In Science Or Industry The Book's Coverage Is Broad, From Supervised Learning (Prediction) To Unsupervised Learning The Many Topics Include Neural Networks, Support Vector Machines, Classification Trees And Boosting---The First Comprehensive Treatment Of This Topic In Any Book This Major New Edition Features Many Topics Not Covered In The Original, Including Graphical Models, Random Forests, Ensemble Methods, Least Angle Regression & Path Algorithms For The Lasso, Non-Negative Matrix Factorization, And Spectral Clustering There Is Also A Chapter On Methods For Wide'' Data (P Bigger Than N), Including Multiple Testing And False Discovery Rates Trevor Hastie, Robert Tibshirani, And Jerome Friedman Are Professors Of Statistics At Stanford University They Are Prominent Researchers In This Area: Hastie And Tibshirani Developed Generalized Additive Models And Wrote A Popular Book Of That Title Hastie Co-Developed Much Of The Statistical Modeling Software And Environment In Rs-Plus And Invented Principal Curves And Surfaces Tibshirani Proposed The Lasso And Is Co-Author Of The Very Successful An Introduction To The Bootstrap Friedman Is The Co-Inventor Of Many Data-Mining Tools Including Cart, Mars, Projection Pursuit And Gradient Boosting Binding:Hardcover, HardcoverPublisher:SpringerPublication Date:2009-02-01Weight:2.6 lbsDimensions:1.5'' H x 9.3'' L x 6.5'' WNumber of Pages:745Language:English
Price: 86.91 USD
Location: USA
End Time: 2024-11-03T15:57:34.000Z
Shipping Cost: 0 USD
Product Images
Item Specifics
Return shipping will be paid by: Buyer
All returns accepted: Returns Accepted
Item must be returned within: 30 Days
Refund will be given as: Money Back
Return policy details:
Book Title: The Elements Of Statistical Learning: Data Mining, Inference...
Number of Pages: Xxii, 745 Pages
Publication Name: Elements of Statistical Learning : Data Mining, Inference, and Prediction
Language: English
Publisher: Springer New York
Item Height: 1.5 in
Subject: Probability & Statistics / General, Intelligence (Ai) & Semantics, Databases / Data Mining
Publication Year: 2009
Type: Textbook
Item Weight: 51.2 Oz
Author: Trevor Hastie, Jerome Friedman, Robert Tibshirani, J. H. Friedman
Subject Area: Mathematics, Computers
Item Length: 9.4 in
Item Width: 6.5 in
Series: Springer Series in Statistics Ser.
Format: Hardcover
