Boosting of Support Vector Machines with application to editing

Pedro A Rangel, Fernando Lozano, Elkin García

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

9 Scopus citations

Abstract

In this paper, we present a weakened variation of Support Vector Machines that can be used together with Adaboost. Our modified Support Vector Machine algorithm has the following interesting properties: First, it is able to handle distributions over the training data. Second, it is a weak algorithm in the sense that it ensures an empirical error upper bounded by 1/2. Third, when used together with Adaboost, the resulting algorithm is faster than the usual SVM training algorithm. Finally, we show that our boosted SVM can be effective as an editing algorithm.

Original languageEnglish (US)
Title of host publicationProceedings - ICMLA 2005
Subtitle of host publicationFourth International Conference on Machine Learning and Applications
Pages374-379
Number of pages6
DOIs
StatePublished - Dec 1 2005
EventICMLA 2005: 4th International Conference on Machine Learning and Applications - Los Angeles, CA, United States
Duration: Dec 15 2005Dec 17 2005

Publication series

NameProceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications
Volume2005

Other

OtherICMLA 2005: 4th International Conference on Machine Learning and Applications
CountryUnited States
CityLos Angeles, CA
Period12/15/0512/17/05

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  • Cite this

    Rangel, P. A., Lozano, F., & García, E. (2005). Boosting of Support Vector Machines with application to editing. In Proceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications (pp. 374-379). [1607478] (Proceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications; Vol. 2005). https://doi.org/10.1109/ICMLA.2005.13