Distributed stochastic subgrandient-based design for support vector machines
Yinghui Wang, Peng Lin, Yiguang Hong · 2017
This paper develops a distributed stochastic subgrandient-based support vector machine algorithm when training data to train support vector machines are distributed in the network. In this situation, all the data are decentralized stored and unavailable to all agents and each agent has to make its own update based on its computation and communication with neighbors. With mild connectivity conditions, we show the convergence of the proposed algorithm even though the network topology is time-varying. Convergent rate is also given for the proposed algorithm. Moreover, we provide numerical simulations on a real classification training set to illustrate the effectiveness of the fully distributed algorithm.