Semisupervised Learning of Classifiers With Application to Human -Computer Interaction

With the growing use of computers and computing objects in the design of many of the day to day tools that humans use, human-computer intelligent interaction is seen as a necessary step for the ability to make computers better aid the human user. There are many tasks involved in designing good interaction between humans and machines. One basic task, related to many such applications, is automatic classification by the machine. Designing a classifier can be done by domain experts or by learning from training data. Training data can be labeled to the different classes or unlabeled. In this work I focus on training probabilistic classifiers with labeled and unlabeled data. I show under what conditions unlabeled data can be used to improve classification performance. I also show that it often occurs that if the conditions are violated, using unlabeled data can be detrimental to the classification performance. I discuss the implications of this analysis when learning a specific type of probabilistic classifiers, namely Bayesian networks, and propose structure learning algorithms that can potentially utilize unlabeled data to improve classification. I show how the theory and algorithms are successfully applied in two applications related to human-computer interaction: facial expression recognition and face detection.