Conventional methods used for the interpretation of activation data provided by functional neuroimaging techniques provide useful insights on what the networks of cerebral structu...
The bias-variance decomposition is a very useful and widely-used tool for understanding machine-learning algorithms. It was originally developed for squared loss. In recent years,...
In this paper, we propose a stochastic version of a general purpose functional programming language as a method of modeling stochastic processes. The language contains random choi...
It is well known that classical set theory is not expressive enough to adequately model categorization and prototype theory. Recent work on compositionality and concept determinat...
The objective of this research is to analyse the ways members of open-source software communities participate in design. In particular we focus on how users of an Open Source (OS) ...