We present a principled Bayesian framework for modeling partial memberships of data points to clusters. Unlike a standard mixture model which assumes that each data point belongs ...
Katherine A. Heller, Sinead Williamson, Zoubin Gha...
Many of today's best classification results are obtained by combining the responses of a set of base classifiers to produce an answer for the query. This paper explores a nov...
The power and popularity of kernel methods stem in part from their ability to handle diverse forms of structured inputs, including vectors, graphs and strings. Recently, several m...
Darrin P. Lewis, Tony Jebara, William Stafford Nob...
Independent Component Analysis is becoming a popular exploratory method for analysing complex data such as that from FMRI experiments. The application of such `model-free' me...
The goal is to build a general framework for ad hoc applications over large networks using semantics and complex systems analysis. The first part deals with semantics because appl...