For object category recognition to scale beyond a small number of classes, it is important that algorithms be able to learn from a small amount of labeled data per additional clas...
Kevin Tang, Marshall Tappen, Rahul Sukthankar, Chr...
The problem of object recognition may be cast into a spatial grammar framework. This system comprises three novel elements: a spatial organisation of line features, an efficient t...
We present a new approximate inference algorithm for Deep Boltzmann Machines (DBM's), a generative model with many layers of hidden variables. The algorithm learns a separate...
In this paper, we develop a general classification framework called Kullback-Leibler Boosting, or KLBoosting. KLBoosting has following properties. First, classification is based o...
Two medical data sets (Breast cancer and Colon cancer) are investigated within a visual data mining paradigm through the unsupervised construction of virtual reality spaces using ...