English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  New Approaches to Statistical Learning Theory

Bousquet, O. (2003). New Approaches to Statistical Learning Theory. Annals of the Institute of Statistical Mathematics, 55(2), 371-389. doi:10.1007/BF02530506.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-DD72-D Version Permalink: http://hdl.handle.net/21.11116/0000-0005-6A8E-5
Genre: Journal Article

Files

show Files

Locators

show
hide
Description:
-

Creators

show
hide
 Creators:
Bousquet, O1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

Content

show
hide
Free keywords: -
 Abstract: We present new tools from probability theory that can be applied to the analysis of learning algorithms. These tools allow to derive new bounds on the generalization performance of learning algorithms and to propose alternative measures of the complexity of the learning task, which in turn can be used to derive new learning algorithms.

Details

show
hide
Language(s):
 Dates: 2003-06
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: BibTex Citekey: 1996
DOI: 10.1007/BF02530506
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Annals of the Institute of Statistical Mathematics
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 55 (2) Sequence Number: - Start / End Page: 371 - 389 Identifier: ISSN: 0563-6841
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000267710