English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Breaking SVM Complexity with Cross-Training

Bakir, G., Bottou, L., & Weston, J. (2005). Breaking SVM Complexity with Cross-Training. Advances in Neural Information Processing Systems, 81-88.

Item is

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Bakir, GH1, Author           
Bottou, L, Author
Weston, J1, Author           
Saul, Editor
L.K., Editor
Weiss, Y., Editor
Bottou, L., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

Content

show
hide
Free keywords: -
 Abstract: We propose an algorithm for selectively removing examples from the training set using probabilistic estimates related to editing algorithms (Devijver and Kittler82). The procedure creates a separable distribution of training examples with minimal impact on the decision boundary position. It breaks the linear dependency between the number of SVs and the number of training examples, and sharply reduces the complexity of SVMs during both the training and prediction stages.

Details

show
hide
Language(s):
 Dates: 2005-07
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 0-262-19534-8
URI: http://books.nips.cc/nips17.html
BibTex Citekey: 2846
 Degree: -

Event

show
hide
Title: Eighteenth Annual Conference on Neural Information Processing Systems (NIPS 2004)
Place of Event: Vancouver, BC, Canada
Start-/End Date: -

Legal Case

show

Project information

show

Source 1

show
hide
Title: Advances in Neural Information Processing Systems
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 81 - 88 Identifier: -