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ESWA
2006
122views more  ESWA 2006»
13 years 9 months ago
Transmembrane segments prediction and understanding using support vector machine and decision tree
In recent years, there have been many studies focusing on improving the accuracy of prediction of transmembrane segments, and many significant results have been achieved. In spite...
Jieyue He, Hae-Jin Hu, Robert W. Harrison, Phang C...
JMLR
2008
116views more  JMLR 2008»
13 years 9 months ago
Support Vector Machinery for Infinite Ensemble Learning
Ensemble learning algorithms such as boosting can achieve better performance by averaging over the predictions of some base hypotheses. Nevertheless, most existing algorithms are ...
Hsuan-Tien Lin, Ling Li
CSDA
2007
105views more  CSDA 2007»
13 years 9 months ago
Model selection for support vector machines via uniform design
The problem of choosing a good parameter setting for a better generalization performance in a learning task is the so-called model selection. A nested uniform design (UD) methodol...
Chien-Ming Huang, Yuh-Jye Lee, Dennis K. J. Lin, S...
NN
2008
Springer
158views Neural Networks» more  NN 2008»
13 years 9 months ago
Improved mapping of information distribution across the cortical surface with the support vector machine
The early visual cortices represent information of several stimulus attributes, such as orientation and color. To understand the coding mechanisms of these attributes in the brain...
Youping Xiao, Ravi Rao, Guillermo A. Cecchi, Ehud ...
TITB
2008
102views more  TITB 2008»
13 years 9 months ago
Nonlinear Support Vector Machine Visualization for Risk Factor Analysis Using Nomograms and Localized Radial Basis Function Kern
Nonlinear classifiers, e.g., support vector machines (SVMs) with radial basis function (RBF) kernels, have been used widely for automatic diagnosis of diseases because of their hig...
Baek Hwan Cho, Hwanjo Yu, Jong Shill Lee, Young Jo...