We propose a unified global entropy reduction maximization (GERM) framework for active learning and semi-supervised learning for speech recognition. Active learning aims to select...
Dong Yu, Balakrishnan Varadarajan, Li Deng, Alex A...
The paper is concerned with two-class active learning. While the common approach for collecting data in active learning is to select samples close to the classification boundary,...
We show how variational Bayesian inference can be implemented for very large generalized linear models. Our relaxation is proven to be a convex problem for any log-concave model. ...
Active learning methods have been considered with an increased interest in the content-based image retrieval (CBIR) community. Those methods used to be based on classical classifi...
Supervised learners can be used to automatically classify many types of spatially distributed data. For example, land cover classification by hyperspectral image data analysis is ...