With the increased availability of data for complex domains, it is desirable to learn Bayesian network structures that are sufficiently expressive for generalization while at the ...
This paper considers a method that combines ideas from Bayesian learning, Bayesian network inference, and classical hypothesis testing to produce a more reliable and robust test o...
We introduce a new metaphor for learning spatial relations--the 3D puzzle. With this metaphor users learn spatial relations by assembling a geometric model themselves. For this pu...
Bernhard Preim, Felix Ritter, Oliver Deussen, Thom...
We develop a novel online learning algorithm for the group lasso in order to efficiently find the important explanatory factors in a grouped manner. Different from traditional bat...
Haiqin Yang, Zenglin Xu, Irwin King, Michael R. Ly...
We present an overview of FAB-MAP, an algorithm for place recognition and mapping developed for infrastructure-free mobile robot navigation in large environments. The system allow...