This work proposes a novel strategy for social learning by introducing the critical feature of
adaptation. In social learning, several distributed agents update continually their belief about …

This work proposes a decentralized architecture, where individual agents aim at solving a
classification problem while observing streaming features of different dimensions and …

In this article, we study the problem of distributed cooperative learning, where a group of
agents seeks to agree on a set of hypotheses that best describes a sequence of private …

Social learning algorithms provide a model for the formation and propagation of opinions
over social networks. However, most studies focus on the case in which agents share their …

This work proposes a new way of combining independently trained classifiers over space
and time. Combination over space means that the outputs of spatially distributed classifiers …

During the past decade, the analysis of distributed systems has seen a dramatic rise of
interest. Applications of distributed learning range from social and sensor networks [2–5], as …

An adaptive network consists of multiple communicating agents, equipped with sensing and
learning abilities that allow them to extract meaningful information from measurements. The …