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127
Voted
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, ...
81
Voted
AUSAI
2007
Springer
15 years 6 months ago
2007
Springer
Dimensional reduction may be effective in order to compress data without loss of essential information. Also, it may be useful in order to smooth data and reduce random noise. The...
102
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KDD
2008
ACM
16 years 1 months ago
2008
ACM
Active learning may hold the key for solving the data scarcity problem in supervised learning, i.e., the lack of labeled data. Indeed, labeling data is a costly process, yet an ac...
108
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MICCAI
2004
Springer
16 years 1 months ago
2004
Springer
We present a novel framework for learning a joint shape and appearance model from a large set of un-labelled training examples in arbitrary positions and orientations. The shape an...
116
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ML
2010
ACM
14 years 7 months ago
2010
ACM
Traditional classification methods assume that the training and the test data arise from the same underlying distribution. However, in several adversarial settings, the test set is...