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JMLR
2010

Consensus-Based Distributed Support Vector Machines

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Consensus-Based Distributed Support Vector Machines
This paper develops algorithms to train support vector machines when training data are distributed across different nodes, and their communication to a centralized processing unit is prohibited due to, for example, communication complexity, scalability, or privacy reasons. To accomplish this goal, the centralized linear SVM problem is cast as a set of decentralized convex optimization subproblems (one per node) with consensus constraints on the wanted classifier parameters. Using the alternating direction method of multipliers, fully distributed training algorithms are obtained without exchanging training data among nodes. Different from existing incremental approaches, the overhead associated with inter-node communications is fixed and solely dependent on the network topology rather than the size of the training sets available per node. Important generalizations to train nonlinear SVMs in a distributed fashion are also developed along with sequential variants capable of online proces...
Pedro A. Forero, Alfonso Cano, Georgios B. Giannak
Added 19 May 2011
Updated 19 May 2011
Type Journal
Year 2010
Where JMLR
Authors Pedro A. Forero, Alfonso Cano, Georgios B. Giannakis
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