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» Is Combining Classifiers Better than Selecting the Best One
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80
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IPCV
2008
14 years 11 months ago
Neonatal Facial Pain Detection Using NNSOA and LSVM
- We report classification experiments using the pilot Infant COPE database of neonatal facial expressions. Two sets of DCT coeffiecents were used to train a neural network simulta...
Sheryl Brahnam, Loris Nanni, Randall S. Sexton
80
Voted
IPSN
2009
Springer
15 years 4 months ago
Automating rendezvous and proxy selection in sensornets
As the diversity of sensornet use cases increases, the combinations of environments and applications that will coexist will make custom engineering increasingly impractical. We in...
David Chu, Joseph M. Hellerstein
ICML
2010
IEEE
14 years 10 months ago
Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets
A sparse representation of Support Vector Machines (SVMs) with respect to input features is desirable for many applications. In this paper, by introducing a 0-1 control variable t...
Mingkui Tan, Li Wang, Ivor W. Tsang
ICC
2007
IEEE
141views Communications» more  ICC 2007»
15 years 3 months ago
Accurate Classification of the Internet Traffic Based on the SVM Method
—The need to quickly and accurately classify Internet traffic for security and QoS control has been increasing significantly with the growing Internet traffic and applications ov...
Zhu Li, Ruixi Yuan, Xiaohong Guan
96
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BMCBI
2007
143views more  BMCBI 2007»
14 years 9 months ago
Gene selection for classification of microarray data based on the Bayes error
Background: With DNA microarray data, selecting a compact subset of discriminative genes from thousands of genes is a critical step for accurate classification of phenotypes for, ...
Ji-Gang Zhang, Hong-Wen Deng