The following two-stage approach to learning from dissimilarity data is described: (1) embed both labeled and unlabeled objects in a Euclidean space; then (2) train a classifier on the labeled objects. The use of linear discriminant analysis for (2), which naturally invites the use of classical...

We introduce a maximum L<italic>q</italic>-likelihood estimation (ML<italic>q</italic>E) of mixture models using our proposed expectation-maximization (EM) algorithm, namely the EM algorithm with L<italic>q</italic>-likelihood (EM-L<italic>q</italic>). Properties of the ML<italic>q</italic>E obtained from the proposed EM-L<italic>q</italic> are studied through simulated mixture model data....

In this article we initiate the study of class cover catch digraphs, a special case of intersection digraphs motivated by applications in machine learning and statistical pattern recognition. Our main result is the exact distribution of the domination number for a data-driven model of random...