21 search results - page 2 / 5 » Active learning for ranking through expected loss optimizati... |
ICML
2001
IEEE
14 years 5 months ago
2001
IEEE
This paper presents an active learning method that directly optimizes expected future error. This is in contrast to many other popular techniques that instead aim to reduce versio...
HICSS
2007
IEEE
13 years 11 months ago
2007
IEEE
Security breaches deter e-commerce activities. Organizations spend millions of dollars on security appliances to make online transactions more secure. Nonetheless, a new virus or ...
CVPR
2009
IEEE
13 years 8 months ago
2009
IEEE
Ranking large scale image and video collections usually expects higher accuracy on top ranked data, while tolerates lower accuracy on bottom ranked ones. In view of this, we propo...
SIGIR
2012
ACM
11 years 7 months ago
2012
ACM
Methods that reduce the amount of labeled data needed for training have focused more on selecting which documents to label than on which queries should be labeled. One exception t...
CIKM
2010
Springer
13 years 3 months ago
2010
Springer
We consider the problem of learning to rank relevant and novel documents so as to directly maximize a performance metric called Expected Global Utility (EGU), which has several de...