We propose an algorithm for the problem of training a SVM model when the set of training examples is horizontally distributed across several data sources. The algorithm requires only one pass through each remote source of training examples, and its accuracy and efficiency follow a clear pattern as function of a user-defined parameter. We outline an agent-based implementation of the algorithm.

L2-SVM Training with Distributed Data

LODI, STEFANO;SARTORI, CLAUDIO
2009

Abstract

We propose an algorithm for the problem of training a SVM model when the set of training examples is horizontally distributed across several data sources. The algorithm requires only one pass through each remote source of training examples, and its accuracy and efficiency follow a clear pattern as function of a user-defined parameter. We outline an agent-based implementation of the algorithm.
Multiagent System Technologies
208
213
S. Lodi; R. Ñanculef; C. Sartori
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/87198
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