To solve the knowledge bottleneck problem, active learning has been widely used for its ability to automatically select the most informative unlabeled examples for human annotation...
Jingbo Zhu, Huizhen Wang, Benjamin K. Tsou, Matthe...
Many unsupervised algorithms for nonlinear dimensionality reduction, such as locally linear embedding (LLE) and Laplacian eigenmaps, are derived from the spectral decompositions o...
This paper addresses the problem of concept sampling. In many real-world applications, a large collection of mixed concepts is available for decision making. However, the collecti...
The domain of Digital Libraries presents specific challenges for unsupervised information extraction to support both the automatic classification of documents and the enhancement ...
Mikalai Krapivin, Maurizio Marchese, Andrei Yadran...
An unsupervised classification algorithm is derived by modeling observed data as a mixture of several mutually exclusive classes that are each described by linear combinations of i...