The term neural network evolution usually refers to network topology evolution leaving the network's parameters to be trained using conventional algorithms. In this paper we ...
Ioannis G. Tsoulos, Dimitris Gavrilis, Euripidis G...
Abstract--The ranking problem has become increasingly important in modern applications of statistical methods in automated decision making systems. In particular, we consider a for...
This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of fini...
Semantic 3D models of buildings encode the geometry as well as the identity of key components of a facility, such as walls, floors, and ceilings. Manually constructing such a mode...
The foremost nonlinear dimensionality reduction algorithms provide an embedding only for the given training data, with no straightforward extension for test points. This shortcomin...