We present a continuous time Bayesian network reasoning and learning engine (CTBN-RLE). A continuous time Bayesian network (CTBN) provides a compact (factored) description of a co...
Christian R. Shelton, Yu Fan, William Lam, Joon Le...
The power of sparse signal coding with learned overcomplete dictionaries has been demonstrated in a variety of applications and fields, from signal processing to statistical infe...
The problem of learning the structure of Bayesian networks from complete discrete data with a limit on parent set size is considered. Learning is cast explicitly as an optimisatio...
In this paper we present a new scheme for detection and tracking of specific objects in a knowledge-based framework. The scheme uses a supervised learning method: Support Vector M...
Lionel Carminati, Jenny Benois-Pineau, Christian J...
Goal-oriented methods have increasingly been recognised as an effective means for eliciting, elaborating, analysing and specifying software requirements. A key activity in these a...
Alessandra Russo, Dalal Alrajeh, Jeff Kramer, Seba...