Data acquisition is a major concern in text classification. The excessive human efforts required by conventional methods to build up quality training collection might not always b...
We address the problem of classification in partially labeled networks (a.k.a. within-network classification) where observed class labels are sparse. Techniques for statistical re...
Brian Gallagher, Hanghang Tong, Tina Eliassi-Rad, ...
Organizing digital images into semantic categories is imperative for effective browsing and retrieval. In large image collections, an efficient algorithm is crucial to quickly cat...
Taufik Abidin, Aijuan Dong, Honglin Li, William Pe...
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 optimal exploitation of the information provided by hyperspectral images requires the development of advanced image processing tools. This paper introduces a new hierarchical ...
Silvia Valero, Philippe Salembier, Jocelyn Chanuss...