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» Theoretical Frameworks for Data Mining
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ICML
2010
IEEE
13 years 7 months ago
Implicit Online Learning
Online learning algorithms have recently risen to prominence due to their strong theoretical guarantees and an increasing number of practical applications for large-scale data ana...
Brian Kulis, Peter L. Bartlett
KDD
2009
ACM
227views Data Mining» more  KDD 2009»
14 years 9 months ago
Efficiently learning the accuracy of labeling sources for selective sampling
Many scalable data mining tasks rely on active learning to provide the most useful accurately labeled instances. However, what if there are multiple labeling sources (`oracles...
Pinar Donmez, Jaime G. Carbonell, Jeff Schneider
IVS
2008
145views more  IVS 2008»
13 years 9 months ago
Towards a taxonomy of movement patterns
A review of research that has been carried out on data mining and visual analysis of movement patterns suggests that there is little agreement on the relevant types of movement pa...
Somayeh Dodge, Robert Weibel, Anna-Katharina Laute...
VIS
2008
IEEE
154views Visualization» more  VIS 2008»
14 years 10 months ago
Volume MLS Ray Casting
The method of Moving Least Squares (MLS) is a popular framework for reconstructing continuous functions from scattered data due to its rich mathematical properties and well-underst...
Christian Ledergerber, Gaël Guennebaud, Miriah ...
CIKM
2009
Springer
14 years 3 months ago
Fragment-based clustering ensembles
Clustering ensembles combine different clustering solutions into a single robust and stable one. Most of existing methods become highly time-consuming when the data size turns to ...
Ou Wu, Mingliang Zhu, Weiming Hu