Learning Bayesian Belief Networks (BBN) from corpora and incorporating the extracted inferring knowledge with a Support Vector Machines (SVM) classifier has been applied to charac...
In order to understand whether conceptual obscurity is truly the reason for the slow uptake of IMS Learning Design (LD), we have initiated an investigation into teachers' unde...
Michael Derntl, Susanne Neumann, Dai Griffiths, Pe...
This paper treats tracking as a foreground/background classification problem and proposes an online semisupervised learning framework. Initialized with a small number of labeled ...
We introduce a new formal model in which a learning algorithm must combine a collection of potentially poor but statistically independent hypothesis functions in order to approxima...
with existing analysis tools. Modular reasoning principles such as abstraction, compositional refinement, and assume-guarantee reasoning are well understood for architectural hiera...