We address the problem of learning topic hierarchies from data. The model selection problem in this domain is daunting—which of the large collection of possible trees to use? We...
David M. Blei, Thomas L. Griffiths, Michael I. Jor...
We consider the model selection problem for support vector machines applied to binary classification. As the data generating process is unknown, we have to rely on heuristics as mo...
This paper reports on an on-going project to investigate techniques to diagnose complex dynamical systems that are modeled as hybrid systems. In particular, we examine continuous s...
Sheila A. McIlraith, Gautam Biswas, Dan Clancy, Vi...
Abstract. We propose a novel method for addressing the model selection problem in the context of kernel methods. In contrast to existing methods which rely on hold-out testing or t...
Abstract— People in densely populated environments typically form groups that split and merge. In this paper we track groups of people so as to reflect this formation process an...
This paper addresses the problem of active model selection for nonlinear dynamical systems. We propose a novel learning approach that selects the most informative subset of time-d...
Alberto Giovanni Busetto, Cheng Soon Ong, Joachim ...
Hidden Markov Models (HMMs) are increasingly being used in computer vision for applications such as: gesture analysis, action recognition from video, and illumination modeling. Th...