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» Accelerated Kernel Feature Analysis
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FLAIRS
2004
13 years 9 months ago
Gene Expression Data Classification with Revised Kernel Partial Least Squares Algorithm
One important feature of the gene expression data is that the number of genes M far exceeds the number of samples N. Standard statistical methods do not work well when N < M. D...
ZhenQiu Liu, Dechang Chen
MR
2007
157views Robotics» more  MR 2007»
13 years 7 months ago
Electronic prognostics - A case study using global positioning system (GPS)
– Prognostic health management (PHM) of electronic systems presents challenges traditionally viewed as either insurmountable or otherwise not worth the cost of pursuit. Recent ch...
Douglas W. Brown, Patrick W. Kalgren, Carl S. Byin...
NIPS
2001
13 years 9 months ago
Discriminative Direction for Kernel Classifiers
In many scientific and engineering applications, detecting and understanding differences between two groups of examples can be reduced to a classical problem of training a classif...
Polina Golland
SC
2005
ACM
14 years 1 months ago
Intelligent Feature Extraction and Tracking for Visualizing Large-Scale 4D Flow Simulations
Terascale simulations produce data that is vast in spatial, temporal, and variable domains, creating a formidable challenge for subsequent analysis. Feature extraction as a data r...
Fan-Yin Tzeng, Kwan-Liu Ma
TNN
2008
182views more  TNN 2008»
13 years 7 months ago
Large-Scale Maximum Margin Discriminant Analysis Using Core Vector Machines
Abstract--Large-margin methods, such as support vector machines (SVMs), have been very successful in classification problems. Recently, maximum margin discriminant analysis (MMDA) ...
Ivor Wai-Hung Tsang, András Kocsor, James T...