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ICML
2008
IEEE
14 years 10 months ago
The asymptotics of semi-supervised learning in discriminative probabilistic models
Semi-supervised learning aims at taking advantage of unlabeled data to improve the efficiency of supervised learning procedures. For discriminative models however, this is a chall...
François Yvon, Nataliya Sokolovska, Olivier...
ICALT
2006
IEEE
14 years 4 months ago
Model-Driven Instructional Engineering to Generate Adaptable Learning Materials
The application of software engineering approaches to generate learning material adapted to a specific instructional purpose presents some issues: of different models, different a...
Juan Manuel Dodero, David Díez
ICALT
2009
IEEE
13 years 7 months ago
Applying Learning Styles to SCORM Compliant Courses
This paper proposes a general framework to develop SCORM compliant courses that provide adaptation according to user learning style. The SCORM standard as well as some of the most...
Ioannis Kazanidis, Maya Satratzemi
ICML
2010
IEEE
13 years 11 months ago
Learning Deep Boltzmann Machines using Adaptive MCMC
When modeling high-dimensional richly structured data, it is often the case that the distribution defined by the Deep Boltzmann Machine (DBM) has a rough energy landscape with man...
Ruslan Salakhutdinov
ICCS
2007
Springer
14 years 4 months ago
Active Learning with Support Vector Machines for Tornado Prediction
In this paper, active learning with support vector machines (SVMs) is applied to the problem of tornado prediction. This method is used to predict which storm-scale circulations yi...
Theodore B. Trafalis, Indra Adrianto, Michael B. R...