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» Making inferences with small numbers of training sets
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HPDC
2007
IEEE
15 years 6 months ago
A fast topology inference: a building block for network-aware parallel processing
Adapting to the network is the key to achieving high performance for communication-intensive applications, including scientific computing, data intensive computing, and multicast...
Tatsuya Shirai, Hideo Saito, Kenjiro Taura
100
Voted
KDD
2009
ACM
204views Data Mining» more  KDD 2009»
16 years 9 days ago
Improving classification accuracy using automatically extracted training data
Classification is a core task in knowledge discovery and data mining, and there has been substantial research effort in developing sophisticated classification models. In a parall...
Ariel Fuxman, Anitha Kannan, Andrew B. Goldberg, R...
TOG
2002
107views more  TOG 2002»
14 years 11 months ago
Ordered and quantum treemaps: Making effective use of 2D space to display hierarchies
Treemaps, a space-filling method of visualizing large hierarchical data sets, are receiving increasing attention. Several algorithms have been proposed to create more useful displ...
Benjamin B. Bederson, Ben Shneiderman, Martin Watt...
CVPR
2005
IEEE
16 years 1 months ago
Online Detection and Classification of Moving Objects Using Progressively Improving Detectors
Boosting based detection methods have successfully been used for robust detection of faces and pedestrians. However, a very large amount of labeled examples are required for train...
Omar Javed, Saad Ali, Mubarak Shah
ICML
2000
IEEE
16 years 17 days ago
Less is More: Active Learning with Support Vector Machines
We describe a simple active learning heuristic which greatly enhances the generalization behavior of support vector machines (SVMs) on several practical document classification ta...
Greg Schohn, David Cohn