ScienceOpen:
research and publishing network
For Publishers
Discovery
Metadata
Peer review
Hosting
Publishing
For Researchers
Join
Publish
Review
Collect
My ScienceOpen
Sign in
Register
Dashboard
Blog
About
Search
Advanced search
My ScienceOpen
Sign in
Register
Dashboard
Search
Search
Advanced search
For Publishers
Discovery
Metadata
Peer review
Hosting
Publishing
For Researchers
Join
Publish
Review
Collect
Blog
About
408
views
0
references
Top references
cited by
1,161
Cite as...
0 reviews
Review
0
comments
Comment
0
recommends
+1
Recommend
0
collections
Add to
0
shares
Share
Twitter
Sina Weibo
Facebook
Email
3,700
similar
All similar
Record
: found
Abstract
: not found
Book
: not found
The Elements of Statistical Learning
other
Author(s):
Trevor Hastie
,
Robert Tibshirani
,
Jerome Friedman
Publication date
(Print):
2009
Publisher:
Springer New York
Read this book at
Publisher
Buy book
Review
Review book
Invite someone to review
Bookmark
Cite as...
There is no author summary for this book yet. Authors can add summaries to their books on ScienceOpen to make them more accessible to a non-specialist audience.
Related collections
Trace Elements and Electrolytes
Author and book information
Book
ISBN (Print):
978-0-387-84857-0
ISBN (Electronic):
978-0-387-84858-7
Publication date (Print):
2009
DOI:
10.1007/978-0-387-84858-7
SO-VID:
f153abad-fa16-45e3-8d6d-806917245512
License:
http://www.springer.com/tdm
History
Data availability:
Comments
Comment on this book
Sign in to comment
Book chapters
pp. 1
Introduction
pp. 1
Overview of Supervised Learning
pp. 1
Introduction
pp. 1
Ensemble Learning
pp. 1
Unsupervised Learning
pp. 9
Overview of Supervised Learning
pp. 43
Linear Methods for Regression
pp. 101
Linear Methods for Classification
pp. 139
Basis Expansions and Regularization
pp. 191
Kernel Smoothing Methods
pp. 219
Model Assessment and Selection
pp. 261
Model Inference and Averaging
pp. 295
Additive Models, Trees, and Related Methods
pp. 337
Boosting and Additive Trees
pp. 389
Neural Networks
pp. 417
Support Vector Machines and Flexible Discriminants
pp. 459
Prototype Methods and Nearest-Neighbors
pp. 485
Unsupervised Learning
pp. 587
Random Forests
pp. 605
Ensemble Learning
pp. 625
Undirected Graphical Models
pp. 649
High-Dimensional Problems: p N
Similar content
3,700
Energy-Corrected Finite Element Methods for Corner Singularities
Authors:
H. Egger
,
U Rude
,
B. Wohlmuth
Statistical Analysis of Yield Trials by AMMI and GGE: Further Considerations
Authors:
Hugh G Gauch
,
Hans-Peter Piepho
,
Paolo Annicchiarico
Minor element relationships in coexisting andalusite and sillimanite
Authors:
Martin Okrusch
,
Bernard Evans
See all similar
Cited by
1,952
Machine Learning in Medicine.
Authors:
Rahul Deo
Calculating the sample size required for developing a clinical prediction model
Authors:
Richard Riley
,
Joie Ensor
,
Kym Snell
…
Resting-state connectivity biomarkers define neurophysiological subtypes of depression
Authors:
Andrew T. Drysdale
,
Logan Grosenick
,
Jonathan Downar
…
See all cited by