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141
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IFIP7
2001
Springer
137views Optimization» more  IFIP7 2001»
15 years 8 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
133
Voted
AUTOMATICA
2005
155views more  AUTOMATICA 2005»
15 years 3 months ago
Identification of MIMO Hammerstein models using least squares support vector machines
This paper studies a method for the identification of Hammerstein models based on Least Squares Support Vector Machines (LS-SVMs). The technique allows for the determination of th...
Ivan Goethals, Kristiaan Pelckmans, Johan A. K. Su...
119
Voted
ICPR
2008
IEEE
15 years 10 months ago
Fast model selection for MaxMinOver-based training of support vector machines
OneClassMaxMinOver (OMMO) is a simple incremental algorithm for one-class support vector classification. We propose several enhancements and heuristics for improving model select...
Fabian Timm, Sascha Klement, Thomas Martinetz
ESANN
2007
15 years 5 months ago
Bat echolocation modelling using spike kernels with Support Vector Regression
Abstract. From the echoes of their vocalisations bats extract information about the positions of reflectors. To gain an understanding of how target position is translated into neu...
Bertrand Fontaine, Herbert Peremans, Benjamin Schr...
TNN
2010
176views Management» more  TNN 2010»
14 years 10 months ago
Sparse approximation through boosting for learning large scale kernel machines
Abstract--Recently, sparse approximation has become a preferred method for learning large scale kernel machines. This technique attempts to represent the solution with only a subse...
Ping Sun, Xin Yao