Hyperparameter design criteria for support vector classifiers
- 1 September 2003
- journal article
- Published by Elsevier in Neurocomputing
- Vol. 55 (1-2) , 109-134
- https://doi.org/10.1016/s0925-2312(03)00430-2
Abstract
No abstract availableThis publication has 12 references indexed in Scilit:
- Model Selection and Error EstimationMachine Learning, 2002
- Choosing Multiple Parameters for Support Vector MachinesMachine Learning, 2002
- K-winner machines for pattern classificationIEEE Transactions on Neural Networks, 2001
- Measuring the VC-Dimension Using Optimized Experimental DesignNeural Computation, 2000
- Circular backpropagation networks embed vector quantizationIEEE Transactions on Neural Networks, 1999
- The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the networkIEEE Transactions on Information Theory, 1998
- A Tutorial on Support Vector Machines for Pattern RecognitionData Mining and Knowledge Discovery, 1998
- Sample compression, learnability, and the Vapnik-Chervonenkis dimensionMachine Learning, 1995
- Support-vector networksMachine Learning, 1995
- Probability Inequalities for Sums of Bounded Random VariablesJournal of the American Statistical Association, 1963