Marcelo Cesar Cirelo

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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)
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 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. This behavior contradicts several empirical results reported in the literature. We present a mathematical analysis(More)
Automatic classification by machines is one of the basic tasks required in any pattern recognition and human computer interaction applications. In this paper we discuss training probabilistic classifiers with labeled and unlabeled data. We provide a new analysis which shows under what conditions unlabeled data can be used in learning to improve(More)
Automatic classification by machines is one of the basic tasks required in any pattern recognition and human computer interaction applications. In this paper we discuss training probabilistic classifiers with labeled and unlabeled data. We provide a new analysis which shows under what conditions unlabeled data can be used in learning to improve(More)
Classifiers based on Bayesian networks are usually learned with a fixed structure or a small subset of possible structures. In the presence of unlabeled data this strategy can be detrimental to classification performance, when the assumed classifier structure is incorrect. In this paper we present a classification driven learning method for Bayesian network(More)
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