247 search results - page 5 / 50 » Superset Learning Based on Generalized Loss Minimization |
ECML
2007
Springer
15 years 4 months ago
2007
Springer
Conventional classification learning allows a classifier to make a one shot decision in order to identify the correct label. However, in many practical applications, the problem ...
COLT
1999
Springer
15 years 2 months ago
1999
Springer
We present a unified framework for reasoning about worst-case regret bounds for learning algorithms. This framework is based on the theory of duality of convex functions. It brin...
KDD
2008
ACM
15 years 10 months ago
2008
ACM
We propose a family of novel cost-sensitive boosting methods for multi-class classification by applying the theory of gradient boosting to p-norm based cost functionals. We establ...
JMLR
2006
14 years 10 months ago
2006
The goal of active learning is to determine the locations of training input points so that the generalization error is minimized. We discuss the problem of active learning in line...
JMLR
2012
13 years 13 days ago
2012
We consider the task of devising large-margin based surrogate losses for the learning to rank problem. In this learning to rank setting, the traditional hinge loss for structured ...