We consider the semi-supervised learning problem, where a decision rule is to be learned from labeled and unlabeled data. In this framework, we motivate minimum entropy regulariza...
Abstract. In this paper, the Ssair (Semi-Supervised Active Image Retrieval) approach, which attempts to exploit unlabeled data to improve the performance of content-based image ret...
In this article, we consider unsupervised learning from the point of view of applying neural computation on signal and data analysis problems. The article is an introductory surve...
We introduce a new collaborative machine learning paradigm in which the user directs a learning algorithm by manually editing the automatically induced model. We identify a generi...
Vittorio Castelli, Lawrence D. Bergman, Daniel Obl...
Learning from imbalanced data occurs frequently in many machine learning applications. One positive example to thousands of negative instances is common in scientific applications...