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Publication
One of the principal bottlenecks in applying learning techniques to classification problems is the large amount of labeled training data required. Especially for images and video, ...
NIPS
1998
1998
Probabilistic Modeling for Face Orientation Discrimination: Learning from Labeled and Unlabeled Data
13 years 8 months ago
This paper presents probabilistic modeling methods to solve the problem of discriminating between five facial orientations with very little labeled data. Three models are explored...
NAACL
2007
13 years 8 months ago
2007
PropBank has been widely used as training data for Semantic Role Labeling. However, because this training data is taken from the WSJ, the resulting machine learning models tend to...
IGARSS
2009
13 years 4 months ago
2009
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 ...
ICML
2009
IEEE
14 years 1 months ago
2009
IEEE
In real sequence labeling tasks, statistics of many higher order features are not sufficient due to the training data sparseness, very few of them are useful. We describe Sparse H...