266 search results - page 7 / 54 » Learning to Identify Unexpected Instances in the Test Set |
KDD
1995
ACM
13 years 11 months ago
1995
ACM
Manydata mining algorithms developed recently are based on inductive learning methods. Very few are based on similarity-based learning. However, similarity-based learning accrues ...
ECML
2003
Springer
14 years 17 days ago
2003
Springer
In multi-instance learning, the training set comprises labeled bags that are composed of unlabeled instances, and the task is to predict the labels of unseen bags. Through analyzin...
PTS
2010
13 years 5 months ago
2010
The addition of a cross-cutting concern in a program, through aspect weaving, has an impact on its existing behaviors. If test cases exist for the program, it is necessary to ident...
ECML
2007
Springer
14 years 1 months ago
2007
Springer
Many real-world classification applications fall into the class of positive and unlabeled (PU) learning problems. In many such applications, not only could the negative training ex...
CVPR
2011
IEEE
13 years 3 months ago
2011
IEEE
Active learning and crowdsourcing are promising ways to efficiently build up training sets for object recognition, but thus far techniques are tested in artificially controlled ...