Name
Abstract | ||
|---|---|---|
This paper extends the closed form calculation of the leave-one-out (LOO) error for least-squares support vector machines (LS-SVMs) from the two-class to the one-class case. Furthermore, it proposes a new algorithm for determining the hyperparameters of a one-class LS-SVM with Gaussian kernels which exploits the efficient LOO error calculation. The standard deviations are selected by prior knowledge while the regularization parameter is optimized in order to obtain a tight decision boundary under the constraint of a zero LOO error. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CICA.2014.7013225 | Computational Intelligence in Control and Automation |
Keywords | Field | DocType |
Gaussian processes,optimisation,pattern classification,regression analysis,support vector machines,unsupervised learning,Gaussian kernels,LOO error calculation,closed form calculation,hyperparameter determination,least-squares support vector machines,one-class LS-SVM,one-class classification,regularization parameter optimization,standard deviation selection,tight decision boundary,unsupervised learning task,zero LOO error constraint,zero leave-one-out error | Pattern recognition,Hyperparameter,Support vector machine,Loo,Algorithm,Gaussian,Regularization (mathematics),Artificial intelligence,Standard deviation,Decision boundary,Mathematics,Leave-one-out error | Conference |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Geritt Kampmann | 1 | 0 | 0.34 |
| Oliver Nelles | 2 | 99 | 17.27 |