DISTRIBUTED CLASSIFICATION LEARNING BASED ON NONLINEAR VECTOR SUPPORT MACHINES FOR SWITCHING NETWORKS

In this paper, we discuss the distributed design for binary classification based on the nonlinear support vector machine in a time-varying multi-agent network when the training data sets are distributeclly located and unavailable to all agents. In particular, the aim is to find a global large margin classifier and then enable each agent to classify any new input data into one of the two labels in the binary classification without sharing its all local data with other agents. We formulate the support vector machine problem into a distributed optimization problem in approximation and employ a distributed algorithm in a time-varying network to solve it. Our algorithm is a stochastic one with the high convergence rate and the low communication cost. With the jointly-connected connectivity condition, we analyze the consensus rate and the convergence rate of the given algorithm. Then some experimental results on various classification training data sets are also provided to illustrate the effectiveness of the given algorithm.