Fábio Gagliardi Cozman

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Overview Graphical models: basic definitions and applications. Inference and learning in credal networks. Quick break: software packages. Formal definitions and computational complexity. Applications. Later Exact and approximate algorithms for reasoning. Agenda Some motivation Graph-theoretical statistical models, directed and undirected Bayesian networks(More)
This paper analyzes the performance of semisupervised learning of mixture models. We show that unlabeled data can lead to an increase in classification error even in situations where additional labeled data would decrease classification error. We present a mathematical analysis of this “degradation” phenomenon and show that it is due to the fact that bias(More)
Understanding human emotions is one of the necessary skills for the computer to interact intelligently with human users. The most expressive way humans display emotions is through facial expressions. In this paper, we report on several advances we have made in building a system for classification of facial expressions from continuous video input. We use(More)
We present new algorithms for inference in credal networksdirected acyclic graphs asso­ ciated with sets of probabilities. Credal networks are here interpreted as encoding strong indepen­ dence relations among variables. We first present a theory of credal networks based on separately specified sets of probabilities. We also show that inference with(More)
Automatic classification is one of the basic tasks required in any pattern recognition and human computer interaction application. In this paper, we discuss training probabilistic classifiers with labeled and unlabeled data. We provide a new analysis that shows under what conditions unlabeled data can be used in learning to improve classification(More)
This paper presents new results on the complexity of graph-theoretical models that represent probabilities (Bayesian networks) and that represent interval and set valued probabilities (credal networks). We define a new class of networks with bounded width, and introduce a new decision problem for Bayesian networks, the maximin a posteriori. We present new(More)