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Boosted Optimization for Network Classification
Journal of machine learning research
In this paper we propose a new classification algorithm designed for application on complex networks motivated by algorithmic similarities between boosting learning and message passing. We consider a network classifier as a logistic regression where the variables define the nodes and the interaction effects define the edges. From this definition we represent the problem as a factor graph of local exponential loss functions. Using the factor graph representation it is possible to interpret thedblp:journals/jmlr/HancockM10 fatcat:n5ffs3bsjzg2reacjymxcsl3hm