Learning support vector machines from distributed data sources

Authors:
Cornelia Caragea;Doina Caragea;Vasant Honavar
Affiliations:
Artificial Intelligence Research Laboratory, Department of Computer Science, Iowa State University, Ames, Iowa;Artificial Intelligence Research Laboratory, Department of Computer Science, Iowa State University, Ames, Iowa;Artificial Intelligence Research Laboratory, Department of Computer Science, Iowa State University, Ames, Iowa
Venue:
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 4
Year:
2005

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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.