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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,...
This paper deals with iterative formation control problems for multi-agent systems with switching topologies. Our approach combines consensus protocol algorithms conducted with an iterative learning control update. This leads to networks dynamically changing with evolution along two directions: a finite time axis and an infinite iteration axis. A distributed algorithm is constructed based on nearest...
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