This paper analyzes the performance of semisupervised learning of mixture models. We show that unlabeled data can lead to an increase in classification error even in situations wh...
Fabio Gagliardi Cozman, Ira Cohen, Marcelo Cesar C...
To address the problem of algorithm selection for the classification task, we equip a relational case base with new similarity measures that are able to cope with multirelational ...
We describe a model of document citation that learns to identify hubs and authorities in a set of linked documents, such as pages retrieved from the world wide web, or papers retr...
The present work is dedicated to the study of modes of data-presentation in the range between text and informant within the framework of inductive inference. In this study, the le...
We describe a probabilistic approach for supervised learning when we have multiple experts/annotators providing (possibly noisy) labels but no absolute gold standard. The proposed...
Vikas C. Raykar, Shipeng Yu, Linda H. Zhao, Anna K...