Constraint classification for multiclass classification and ranking

Sariel Har-Peled, Dan Roth, Dav Zimak

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

Abstract

The constraint classification framework captures many flavors of multiclass classification including winner-take-all multiclass classification, multilabel classification and ranking. We present a meta-algorithm for learning in this framework that learns via a single linear classifier in high dimension. We discuss distribution independent as well as margin-based generalization bounds and present empirical and theoretical evidence showing that constraint classification benefits over existing methods of multiclass classification.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 15 - Proceedings of the 2002 Conference, NIPS 2002
PublisherNeural information processing systems foundation
ISBN (Print)0262025507, 9780262025508
StatePublished - 2003
Event16th Annual Neural Information Processing Systems Conference, NIPS 2002 - Vancouver, BC, Canada
Duration: Dec 9 2002Dec 14 2002

Other

Other16th Annual Neural Information Processing Systems Conference, NIPS 2002
CountryCanada
CityVancouver, BC
Period12/9/0212/14/02

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ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

Cite this

Har-Peled, S., Roth, D., & Zimak, D. (2003). Constraint classification for multiclass classification and ranking. In Advances in Neural Information Processing Systems 15 - Proceedings of the 2002 Conference, NIPS 2002 Neural information processing systems foundation.