Stochastic optimization problems attempt to model uncertainty in the data by assuming that (part of) the input is specified in terms of a probability distribution. We consider the...
Abstract. We formulate the problem of least squares temporal difference learning (LSTD) in the framework of least squares SVM (LS-SVM). To cope with the large amount (and possible ...
Abstract. In this paper, we describe an unsupervised learning framework to segment a scene into semantic regions and to build semantic scene models from longterm observations of mo...
In this work, by using the local node refinement technique purposed in [2, 1], and a quad-tree type algorithm [3, 13], we built a global refinement technique for Kansa's unsy...
Three dimensional computer reconstruction provides us with a means of visualising past environments, allowing us a glimpse of the past that might otherwise be difficult to appreci...