While clustering is usually an unsupervised operation, there are circumstances in which we believe (with varying degrees of certainty) that items A and B should be assigned to the...
In this paper, we propose a new context-sensitive Bayesian learning algorithm. By modeling the distributions of data locations by a mixture of Gaussians, the new algorithm can uti...
In this paper we propose a new criterion, based on Minimum Description Length (MDL), to estimate an optimal number of clusters. This criterion, called Kernel MDL (KMDL), is particu...
Ivan O. Kyrgyzov, Olexiy O. Kyrgyzov, Henri Ma&ici...
We present a framework for audio background modeling of complex and unstructured audio environments. The determination of background audio is important for understanding and predi...
We present a method for learning a human understandable, executable model of an agent's behavior using observations of its interaction with the environment. By executable we ...
Andrew Guillory, Hai Nguyen, Tucker R. Balch, Char...