Abstract. We present a study which compares human-human computermediated tutoring with two computer tutoring systems based on the same materials but differing in the type of feedba...
Myroslava Dzikovska, Natalie B. Steinhauser, Johan...
We present conditional random fields, a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hid...
John D. Lafferty, Andrew McCallum, Fernando C. N. ...
The second author has put forward a theory of incomplete interval probabilities meant to give a common framework to both interval probabilities and openframe bodies of evidence, a...
We introduce an expectation maximizationtype (EM) algorithm for maximum likelihood optimization of conditional densities. It is applicable to hidden variable models where the dist...
—Approaches based on conditional independence tests are among the most popular methods for learning graphical models from data. Due to the predominance of Bayesian networks in th...