Reference TypeConference Proceedings
Citation KeyRauberBern:2011:KeMuPe
Author1 Rauber, Thomas W.
2 Berns, Karsten
Affiliation1 Departamento de Informática, Centro Tecnológico, Universidade Federal do Espírito Santo
2 Robotics Research Lab, Department of Computer Science, University of Kaiserslautern, Gottlieb-Daimler-Strasse, 67663 Kaiserslautern, Germany
TitleKernel Multilayer Perceptron
Conference NameConference on Graphics, Patterns and Images, 24 (SIBGRAPI)
EditorLewiner, Thomas
Torres, Ricardo
Book TitleProceedings
DateAug. 28 - 31, 2011
Publisher CityLos Alamitos
PublisherIEEE Computer Society
Conference LocationMaceió
KeywordsMultilayer Perceptron, kernel mapping.
AbstractWe enhance the Multilayer Perceptron to map a feature vector not only from the original d-dimensional feature space, but from an intermediate implicit Hilbert feature space in which kernels calculate inner products. The kernel substitutes the usual inner product between weight vectors and the input vector (or the feature vector of the hidden layer). The objective is to boost the generalization capability of this universal function approximator even more. Classification experiments with standard Machine Learning data sets are shown. We are able to improve the classification accuracy performance criterion for certain kernel types and their intrinsic parameters for the majority of the data sets.
Tertiary TypeFull Paper
FormatDVD, On-line.
Size154 KiB
Number of Files1
Target File86589.pdf
Last Update2011:
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