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 / S. Lodi; R. Ñanculef; C. Sartori. - STAMPA. - 5774:(2009), pp. 208-213. (Intervento presentato al convegno Multiagent System Technologies 7th German Conference, MATES 2009 tenutosi a Hamburg, Germany nel September 9-11, 2009).
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
