On Maximum Margin Hierarchical Classification
Rousu, J., Saunders, C., Szedmak, S. and Shawe-Taylor, J. (2004) On Maximum Margin Hierarchical Classification. At Workshop on Learning with Structured Outputs at NIPS 2004, Whistler,
Download
![[img]](http://eprints.soton.ac.uk/style/images/fileicons/application_pdf.png) |
PDF
Download (192Kb) |
Description/Abstract
We present work in progress towards maximum margin hierarchical classification where the objects are allowed to belong to more than one category at a time. The classification hierarchy is represented as a Markov network equipped with an exponential family defined on the edges. We present a variation of the maximum margin multilabel learning framework, suited to the hierarchical classification task and allows efficient implementation via gradient-based methods. We compare the behaviour of the proposed method to the recently introduced hierarchical regularized least squares classifier as well as two SVM variants in Reuter's news article classification.
| Item Type: | Conference or Workshop Item (Speech) |
|---|---|
| Additional Information: | Event Dates: December 2004 |
| Related URLs: | |
| Divisions: | Faculty of Physical and Applied Science > Electronics and Computer Science |
| Item ID: | 260253 |
| Date Deposited: | 11 Jan 2005 |
| Last Modified: | 02 Mar 2012 14:04 |
| Contributors: | Rousu, J. (Author) Saunders, C. (Author) Szedmak, S. (Author) Shawe-Taylor, J. (Author) |
| Date: | 2004 |
| Additional Information: | Event Dates: December 2004 |
| Status: | Published |
| Further Information: | Google Scholar |
| URI: | http://eprints.soton.ac.uk/id/eprint/260253 |
Actions (login required)
 |
View Item |
