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 distributedly 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...