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We present a distributed machine learning framework based on support vector machines that allows classification problems to be solved iteratively through parallel update algorithms with minimal communication overhead. Decomposing the main problem into multiple relaxed subproblems allows them to be simultaneously solved by individual computing units operating in parallel and having access to only a subset of the data. A sufficient condition is derived under which a synchronous, discrete-timedoi:10.1109/icpr.2008.4761268 dblp:conf/icpr/AlpcanB08 fatcat:6fcpz55gffbxtb2j2zwrf34mlm