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» Experimental perspectives on learning from imbalanced data
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ML
2010
ACM
151views Machine Learning» more  ML 2010»
14 years 8 months ago
Inductive transfer for learning Bayesian networks
In several domains it is common to have data from different, but closely related problems. For instance, in manufacturing, many products follow the same industrial process but with...
Roger Luis, Luis Enrique Sucar, Eduardo F. Morales
INFOCOM
2011
IEEE
14 years 1 months ago
User-centric data dissemination in disruption tolerant networks
Abstract—Data dissemination is useful for many applications of Disruption Tolerant Networks (DTNs). Current data dissemination schemes are generally network-centric ignoring user...
Wei Gao, Guohong Cao
KDD
2009
ACM
269views Data Mining» more  KDD 2009»
15 years 10 months ago
Frequent pattern mining with uncertain data
In this paper, we will examine the frequent pattern mining for uncertain data sets. We will show how the broad classes of algorithms can be extended to the uncertain data setting....
Charu C. Aggarwal, Yan Li, Jianyong Wang, Jing Wan...
ALT
2007
Springer
15 years 4 months ago
On Universal Transfer Learning
In transfer learning the aim is to solve new learning tasks using fewer examples by using information gained from solving related tasks. Existing transfer learning methods have be...
M. M. Hassan Mahmud
JMLR
2010
177views more  JMLR 2010»
14 years 4 months ago
Multitask Learning for Brain-Computer Interfaces
Brain-computer interfaces (BCIs) are limited in their applicability in everyday settings by the current necessity to record subjectspecific calibration data prior to actual use of...
Morteza Alamgir, Moritz Grosse-Wentrup, Yasemin Al...