Sparse graphical models have proven to be a flexible class of multivariate probability models for approximating high-dimensional distributions. In this paper, we propose techniques...
Vincent Y. F. Tan, Sujay Sanghavi, John W. Fisher ...
In machine learning problems with tens of thousands of features and only dozens or hundreds of independent training examples, dimensionality reduction is essential for good learni...
One important problem in machine learning is how to extract knowledge from prior experience, then transfer and apply this knowledge in new learning tasks. To address this problem, ...
This article presents considerations about viability on reutilize existing web based e-Learning systems on Interactive Digital TV environment according to Digital TV standard adopt...
Barbara De Franco, Hilda Carvalho Oliveira, Everal...
This paper presents a dissertation project on business-integrated, service-oriented learning architectures. The isolation of corporate learning management from core business functi...