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Boosted optimization for network classification

Boosted optimization for network classification

This video was recorded at 13th International Conference on Artificial Intelligence and Statistics (AISTATS), Sardinia 2010. 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 the network classifier as an ensemble of individual node classifiers. We then combine ideas from boosted learning with network optimization algorithms to define two novel algorithms,... Show More


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