LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Drezet, P.; Harrison, R.F. (1998)
Publisher: Department of Automatic Control and Systems Engineering
Languages: English
Types: Book
Subjects:

Classified by OpenAIRE into

ACM Ref: ComputingMethodologies_PATTERNRECOGNITION
A new method of implementing Support Vector learning algorithms for classification and regression is presented which deals with problems of over-defined solutions and excessive complexity. Classification problems are solved with the minimum number of support vectors, irrespective of over-lapping training data. Support vector regression can be solved as a sparse solution, without requiring an e-insensitive zone. The optimisation method is generalised to include control of sparsity for both support vector classification and regression.
  • No references.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article