153 search results - page 24 / 31 » Semi-supervised learning by disagreement |
KAIS
2010
13 years 8 months ago
2010
In many real-world tasks there are abundant unlabeled examples but the number of labeled training examples is limited, because labeling the examples requires human efforts and exp...
CLEF
2010
Springer
13 years 11 months ago
2010
Springer
In this paper, we present the LIP6 annotation models for the ImageCLEFannotation 2010 task. We study two methods to train and merge the results of different classifiers in order to...
KDD
2002
ACM
14 years 10 months ago
2002
ACM
An adaptive semi-supervised ensemble method, ASSEMBLE, is proposed that constructs classification ensembles based on both labeled and unlabeled data. ASSEMBLE alternates between a...
CIKM
2008
Springer
13 years 11 months ago
2008
Springer
Statistical machine learning techniques for data classification usually assume that all entities are i.i.d. (independent and identically distributed). However, real-world entities...
NIPS
2004
13 years 11 months ago
2004
In the context of binary classification, we define disagreement as a measure of how often two independently-trained models differ in their classification of unlabeled data. We exp...