45 search results - page 7 / 9 » Boosting in the Limit: Maximizing the Margin of Learned Ense... |
MCS
2002
Springer
13 years 7 months ago
2002
Springer
Bagging and boosting are two popular ensemble methods that achieve better accuracy than a single classifier. These techniques have limitations on massive datasets, as the size of t...
TNN
2010
13 years 2 months ago
2010
Abstract--Recently, sparse approximation has become a preferred method for learning large scale kernel machines. This technique attempts to represent the solution with only a subse...
CSL
2010
Springer
13 years 7 months ago
2010
Springer
We propose a unified global entropy reduction maximization (GERM) framework for active learning and semi-supervised learning for speech recognition. Active learning aims to select...
ICASSP
2009
IEEE
14 years 2 months ago
2009
IEEE
We propose a new active learning algorithm to address the problem of selecting a limited subset of utterances for transcribing from a large amount of unlabeled utterances so that ...
ECCV
2010
Springer
14 years 1 months ago
2010
Springer
Many learning tasks for computer vision problems can be described by multiple views or multiple features. These views can be exploited in order to learn from unlabeled data, a.k.a....