We consider clustering situations in which the pairwise affinity between data points depends on a latent ”context” variable. For example, when clustering features arising fro...
This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. ...
Kamal Nigam, Andrew McCallum, Sebastian Thrun, Tom...
The goal of this article is to present an effective and robust tracking algorithm for nonlinear feet motion by deploying particle filter integrated with Gaussian process latent v...
Text classification using a small labeled set and a large unlabeled data is seen as a promising technique to reduce the labor-intensive and time consuming effort of labeling traini...
This paper presents a novel methodology to infer parameters of probabilistic models whose output noise is a Student-t distribution. The method is an extension of earlier work for ...