Learning Support Vector Machines from distributed data sources

Cornelia Caragea, Doina Caragea, Vasant Honavar

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

16 Scopus citations

Abstract

In this paper we address the problem of learning Support Vector Machine (SVM) classifiers from distributed data sources. We identify sufficient statistics for learning SVMs and present an algorithm that learns SVMs from distributed data by iteratively computing the set of sufficient statistics. We prove that our algorithm is exact with respect to its centralized counterpart and efficient in terms of time complexity.

Original languageEnglish (US)
Title of host publicationProceedings of the 20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference, AAAI-05/IAAI-05
Pages1602-1603
Number of pages2
Volume4
StatePublished - 2005
Event20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference, AAAI-05/IAAI-05 - Pittsburgh, PA, United States
Duration: Jul 9 2005Jul 13 2005

Other

Other20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference, AAAI-05/IAAI-05
CountryUnited States
CityPittsburgh, PA
Period7/9/057/13/05

All Science Journal Classification (ASJC) codes

  • Software

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