Improved output coding for classification using continuous relaxation

Koby Crammer, Yoram Singer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Output coding is a general method for solving multiclass problems by reducing them to multiple binary classification problems. Previous research on output coding has employed, almost solely, predefined discrete codes. We describe an algorithm that improves the performance of output codes by relaxing them to continuous codes. The relaxation procedure is cast as an optimization problem and is reminiscent of the quadratic program for support vector machines. We describe experiments with the proposed algorithm, comparing it to standard discrete output codes. The experimental results indicate that continuous relaxations of output codes often improve the generalization performance, especially for short codes.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 13 - Proceedings of the 2000 Conference, NIPS 2000
PublisherNeural information processing systems foundation
ISBN (Print)0262122413, 9780262122412
StatePublished - 2001
Event14th Annual Neural Information Processing Systems Conference, NIPS 2000 - Denver, CO, United States
Duration: Nov 27 2000Dec 2 2000

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Other

Other14th Annual Neural Information Processing Systems Conference, NIPS 2000
Country/TerritoryUnited States
CityDenver, CO
Period11/27/0012/2/00

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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