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CDC
2009
IEEE
117views Control Systems» more  CDC 2009»
14 years 3 months ago
Risk sensitive robust support vector machines
— We propose a new family of classification algorithms in the spirit of support vector machines, that builds in non-conservative protection to noise and controls overfitting. O...
Huan Xu, Constantine Caramanis, Shie Mannor, Sungh...
ICANN
2001
Springer
14 years 2 months ago
Learning and Prediction of the Nonlinear Dynamics of Biological Neurons with Support Vector Machines
Based on biological data we examine the ability of Support Vector Machines (SVMs) with gaussian kernels to learn and predict the nonlinear dynamics of single biological neurons. We...
Thomas Frontzek, Thomas Navin Lal, Rolf Eckmiller
IFIP7
2001
Springer
137views Optimization» more  IFIP7 2001»
14 years 2 months ago
Data Mining via Support Vector Machines
Support vector machines (SVMs) have played a key role in broad classes of problems arising in various fields. Much more recently, SVMs have become the tool of choice for problems...
Olvi L. Mangasarian
NLPRS
2001
Springer
14 years 2 months ago
Unknown Word Guessing and Part-of-Speech Tagging Using Support Vector Machines
The accuracy of part-of-speech (POS) tagging for unknown words is substantially lower than that for known words. Considering the high accuracy rate of up-to-date statistical POS t...
Tetsuji Nakagawa, Taku Kudo, Yuji Matsumoto
ANNPR
2006
Springer
14 years 2 months ago
Support Vector Regression Using Mahalanobis Kernels
Abstract. In our previous work we have shown that Mahalanobis kernels are useful for support vector classifiers both from generalization ability and model selection speed. In this ...
Yuya Kamada, Shigeo Abe