In this paper we examine a novel addition to the known methods for learning Bayesian networks from data that improves the quality of the learned networks. Our approach explicitly ...
We investigate the use of certain data-dependent estimates of the complexity of a function class, called Rademacher and Gaussian complexities. In a decision theoretic setting, we ...
This paper addresses recognition of human activities with stochastic structure, characterized by variable spacetime arrangements of primitive actions, and conducted by a variable ...
: This paper discusses the interplay between networks and control systems. As we gain more understanding about the structure and dynamics of physical networks, their effects on the...
A. Clauset, Herbert G. Tanner, Chaouki T. Abdallah...
Many data objects in the real world have attributes about location and time. Such spatiotemporal objects can be found in applications such Geographic Information Systems (GIS), env...