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» Learning with Few Examples by Transferring Feature Relevance
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BMCBI
2008
143views more  BMCBI 2008»
14 years 9 months ago
Automatic detection of exonic splicing enhancers (ESEs) using SVMs
Background: Exonic splicing enhancers (ESEs) activate nearby splice sites and promote the inclusion (vs. exclusion) of exons in which they reside, while being a binding site for S...
Britta Mersch, Alexander Gepperth, Sándor S...
CVPR
2007
IEEE
15 years 1 months ago
Diverse Active Ranking for Multimedia Search
Interactively learning from a small sample of unlabeled examples is an enormously challenging task, one that often arises in vision applications. Relevance feedback and more recen...
ShyamSundar Rajaram, Charlie K. Dagli, Nemanja Pet...
IJCV
2008
192views more  IJCV 2008»
14 years 9 months ago
Learning to Locate Informative Features for Visual Identification
Object identification (OID) is specialized recognition where the category is known (e.g. cars) and the algorithm recognizes an object's exact identity (e.g. Bob's BMW). ...
Andras Ferencz, Erik G. Learned-Miller, Jitendra M...
CVPR
2008
IEEE
15 years 11 months ago
Looking around the backyard helps to recognize faces and digits
Human beings have the ability to learn to recognize a new visual category based on only one or few training examples. Part of this ability might come from the use of knowledge fro...
Honghao Shan, Garrison W. Cottrell
RULEML
2004
Springer
15 years 3 months ago
Rule Learning for Feature Values Extraction from HTML Product Information Sheets
The Web is now a huge information repository with a rich semantic structure that, however, is primarily addressed to human understanding rather than automated processing by a compu...
Costin Badica, Amelia Badica