278 search results - page 37 / 56 » Strong Separation of Learning Classes |
SIGIR
2008
ACM
14 years 9 months ago
2008
ACM
It is difficult to apply machine learning to new domains because often we lack labeled problem instances. In this paper, we provide a solution to this problem that leverages domai...
CORR
2011
Springer
14 years 1 months ago
2011
Springer
Boosting is a popular way to derive powerful learners from simpler hypothesis classes. Following previous work (Mason et al., 1999; Friedman, 2000) on general boosting frameworks,...
63
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CVPR
1999
IEEE
15 years 11 months ago
1999
IEEE
The existence of large image datasets such as the set of photos on the World Wide Web make it possible to build powerful generic models for low-level image attributes like color u...
128
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ECML
2007
Springer
15 years 3 months ago
2007
Springer
Boosting methods are known to exhibit noticeable overfitting on some datasets, while being immune to overfitting on other ones. In this paper we show that standard boosting algorit...
116
Voted
CVPR
2009
IEEE
16 years 4 months ago
2009
IEEE
Active learning strategies can be useful when manual labeling
effort is scarce, as they select the most informative
examples to be annotated first. However, for visual category
...