Leveraged vector machines

Yoram Singer

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

8 Scopus citations

Abstract

We describe an iterative algorithm for building vector machines used in classification tasks. The algorithm builds on ideas from support vector machines, boosting, and generalized additive models. The algorithm can be used with various continuously differential functions that bound the discrete (0-1) classification loss and is very simple to implement. We test the proposed algorithm with two different loss functions on synthetic and natural data. We also describe a norm-penalized version of the algorithm for the exponential loss function used in AdaBoost. The performance of the algorithm on natural data is comparable to support vector machines while typically its running time is shorter than of SVM.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 12 - Proceedings of the 1999 Conference, NIPS 1999
PublisherNeural information processing systems foundation
Pages610-616
Number of pages7
ISBN (Print)0262194503, 9780262194501
StatePublished - Jan 1 2000
Externally publishedYes
Event13th Annual Neural Information Processing Systems Conference, NIPS 1999 - Denver, CO, United States
Duration: Nov 29 1999Dec 4 1999

Publication series

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

Other

Other13th Annual Neural Information Processing Systems Conference, NIPS 1999
CountryUnited States
CityDenver, CO
Period11/29/9912/4/99

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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  • Cite this

    Singer, Y. (2000). Leveraged vector machines. In Advances in Neural Information Processing Systems 12 - Proceedings of the 1999 Conference, NIPS 1999 (pp. 610-616). (Advances in Neural Information Processing Systems). Neural information processing systems foundation.