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GECCO
2007
Springer
184views Optimization» more  GECCO 2007»
13 years 10 months ago
Evolving kernels for support vector machine classification
While support vector machines (SVMs) have shown great promise in supervised classification problems, researchers have had to rely on expert domain knowledge when choosing the SVM&...
Keith Sullivan, Sean Luke
ICML
2005
IEEE
14 years 7 months ago
Adapting two-class support vector classification methods to many class problems
A geometric construction is presented which is shown to be an effective tool for understanding and implementing multi-category support vector classification. It is demonstrated ho...
Simon I. Hill, Arnaud Doucet
CDC
2010
IEEE
155views Control Systems» more  CDC 2010»
13 years 1 months ago
Linear parametric noise models for Least Squares Support Vector Machines
In the identification of nonlinear dynamical models it may happen that not only the system dynamics have to be modeled but also the noise has a dynamic character. We show how to ad...
Tillmann Falck, Johan A. K. Suykens, Bart De Moor
TNN
2008
97views more  TNN 2008»
13 years 6 months ago
Training Hard-Margin Support Vector Machines Using Greedy Stagewise Algorithm
Hard-margin support vector machines (HM-SVMs) suffer from getting overfitting in the presence of noise. Soft-margin SVMs deal with this problem by introducing a regularization term...
Liefeng Bo, Ling Wang, Licheng Jiao
SDM
2009
SIAM
191views Data Mining» more  SDM 2009»
14 years 4 months ago
Adaptive Concept Drift Detection.
An established method to detect concept drift in data streams is to perform statistical hypothesis testing on the multivariate data in the stream. Statistical decision theory off...
Anton Dries, Ulrich Rückert