We propose an unsupervised, probabilistic method for learning visual feature hierarchies. Starting from local, low-level features computed at interest point locations, the method c...
Graph-based methods for semi-supervised learning have recently been shown to be promising for combining labeled and unlabeled data in classification problems. However, inference f...
In the process of developing Web Based Training (WBT) applications, the Sharable Content Object Reference Model (SCORM) has become the most common eLearning standard making it pos...
In this paper we present a meeting state recognizer based on a combination of multi-modal sensor data in a smart room. Our approach is based on the training of a statistical model ...
Recent work has exploited boundedness of data in the unsupervised learning of new types of generative model. For nonnegative data it was recently shown that the maximum-entropy ge...