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TNN
2010
234views Management» more  TNN 2010»
12 years 11 months ago
Novel maximum-margin training algorithms for supervised neural networks
This paper proposes three novel training methods, two of them based on the back-propagation approach and a third one based on information theory for Multilayer Perceptron (MLP) bin...
Oswaldo Ludwig, Urbano Nunes
JMLR
2010
108views more  JMLR 2010»
12 years 11 months ago
Tree Decomposition for Large-Scale SVM Problems
To handle problems created by large data sets, we propose a method that uses a decision tree to decompose a given data space and train SVMs on the decomposed regions. Although the...
Fu Chang, Chien-Yang Guo, Xiao-Rong Lin, Chi-Jen L...
ICPR
2008
IEEE
13 years 11 months ago
A fast revised simplex method for SVM training
Active set methods for training the Support Vector Machines (SVM) are advantageous since they enable incremental training and, as we show in this research, do not exhibit exponent...
Christopher Sentelle, Georgios C. Anagnostopoulos,...
ICPR
2008
IEEE
13 years 11 months ago
Kernel Bisecting k-means clustering for SVM training sample reduction
This paper presents a new algorithm named Kernel Bisecting k-means and Sample Removal (KBK-SR) as a sampling preprocessing for SVM training to improve the scalability. The novel c...
Xiao-Zhang Liu, Guo-Can Feng
EMO
2009
Springer
147views Optimization» more  EMO 2009»
13 years 11 months ago
Application of MOGA Search Strategy to SVM Training Data Selection
When training Support Vector Machine (SVM), selection of a training data set becomes an important issue, since the problem of overfitting exists with a large number of training da...
Tomoyuki Hiroyasu, Masashi Nishioka, Mitsunori Mik...