English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
  Building Support Vector Machines with Reduced Classifier Complexity

Keerthi, S., Chapelle, O., & DeCoste, D. (2006). Building Support Vector Machines with Reduced Classifier Complexity. Journal of Machine Learning Research, 7, 1493-1515. Retrieved from http://jmlr.csail.mit.edu/papers/volume7/keerthi06a/keerthi06a.pdf.

Item is

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Keerthi, S, Author
Chapelle, O1, Author           
DeCoste, D, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

Content

show
hide
Free keywords: -
 Abstract: Support vector machines (SVMs), though accurate, are not preferred in applications requiring great classification speed, due to the number of support vectors being large. To overcome this problem we devise a primal method with the following properties: (1) it decouples the idea of basis functions from the concept of support vectors; (2) it greedily finds a set of kernel basis functions of a specified maximum size (dmax) to approximate the SVM primal cost function well; (3) it is efficient and roughly scales as O(ndmax^2) where n is the number of training examples; and, (4) the number of basis functions it requires to achieve an accuracy close to the SVM accuracy is usually far less than the number of SVM support vectors.

Details

show
hide
Language(s):
 Dates: 2006-07
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Journal of Machine Learning Research
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 7 Sequence Number: - Start / End Page: 1493 - 1515 Identifier: -