In this paper we propose a Bayesian framework for XCS [9], called BXCS. Following [4], we use probability distributions to represent the uncertainty over the classifier estimates ...
Davide Aliprandi, Alex Mancastroppa, Matteo Matteu...
We propose a new graph-based semisupervised learning (SSL) algorithm and demonstrate its application to document categorization. Each document is represented by a vertex within a ...
Morfette is a modular, data-driven, probabilistic system which learns to perform joint morphological tagging and lemmatization from morphologically annotated corpora. The system i...
Grzegorz Chrupala, Georgiana Dinu, Josef van Genab...
‘Knowledge sharing’ and ‘learning’ are terms often connected with the ‘New office’, the “modern” open office space. Work in these settings becomes more and more dis...
While CSCW research has mostly been focusing on desktop applications there is a growing interest on ubiquitous and tangible computing. We present ethnographic fieldwork and prototy...