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ECML
2003
Springer
14 years 28 days ago
Support Vector Machines with Example Dependent Costs
Abstract. Classical learning algorithms from the fields of artificial neural networks and machine learning, typically, do not take any costs into account or allow only costs depe...
Ulf Brefeld, Peter Geibel, Fritz Wysotzki
ICPR
2002
IEEE
14 years 19 days ago
Tangent Distance Kernels for Support Vector Machines
When dealing with pattern recognition problems one encounters different types of a-priori knowledge. It is important to incorporate such knowledge into the classification method ...
Bernard Haasdonk, Daniel Keysers
CDC
2009
IEEE
117views Control Systems» more  CDC 2009»
14 years 12 days 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 5 days 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 4 days 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