33 search results - page 2 / 7 » Exploiting Unlabeled Data to Enhance Ensemble Diversity |
CEC
2009
IEEE
13 years 8 months ago
2009
IEEE
—When performing predictive data mining, the use of ensembles is known to increase prediction accuracy, compared to single models. To obtain this higher accuracy, ensembles shoul...
NIPS
2008
13 years 7 months ago
2008
The cluster assumption is exploited by most semi-supervised learning (SSL) methods. However, if the unlabeled data is merely weakly related to the target classes, it becomes quest...
IJSI
2008
13 years 5 months ago
2008
Many data mining applications have a large amount of data but labeling data is often difficult, expensive, or time consuming, as it requires human experts for annotation. Semi-supe...
TON
2012
11 years 8 months ago
2012
Abstract—Wireless networks (including wireless mesh networks) provide opportunities for using multiple paths. Multihoming of hosts, possibly using different technologies and prov...
ICPR
2010
IEEE
13 years 3 months ago
2010
IEEE
Abstract--In this paper we propose a new multi-view semisupervised learning algorithm called Local Co-Training (LCT). The proposed algorithm employs a set of local models with vect...