Cascade jump support vector machine classifiers

Sourabh Ravindran, David V. Anderson, James Rehg

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

Abstract

In this paper we present a new support vector machine (SVM) based classifier that is able to achieve better generalization as compared to the standard SVM. Better generalization is achieved by using a cascade of modified proximal SVMs to remove simpler examples before presenting the difficult examples to a more complex SVM. The cascade structure uses the discrimination afforded by different feature spaces (by using different kernels) to simplify the classification task.

Original languageEnglish (US)
Title of host publication2005 IEEE Workshop on Machine Learning for Signal Processing
Pages135-139
Number of pages5
DOIs
StatePublished - 2005
Externally publishedYes
Event2005 IEEE Workshop on Machine Learning for Signal Processing - Mystic, CT, United States
Duration: Sep 28 2005Sep 30 2005

Publication series

Name2005 IEEE Workshop on Machine Learning for Signal Processing

Other

Other2005 IEEE Workshop on Machine Learning for Signal Processing
Country/TerritoryUnited States
CityMystic, CT
Period9/28/059/30/05

ASJC Scopus subject areas

  • General Engineering

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