@InProceedings{RauberBern:2011:KeMuPe,
author = "Rauber, Thomas W. and Berns, Karsten",
affiliation = "Departamento de Inform{\'a}tica, Centro Tecnol{\'o}gico,
Universidade Federal do Esp{\'{\i}}rito Santo and Robotics
Research Lab, Department of Computer Science, University of
Kaiserslautern, Gottlieb-Daimler-Strasse, 67663 Kaiserslautern,
Germany",
title = "Kernel Multilayer Perceptron",
booktitle = "Proceedings...",
year = "2011",
editor = "Lewiner, Thomas and Torres, Ricardo",
organization = "Conference on Graphics, Patterns and Images, 24. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "Multilayer Perceptron, kernel mapping.",
abstract = "We 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.",
conference-location = "Macei{\'o}, AL, Brazil",
conference-year = "28-31 Aug. 2011",
doi = "10.1109/SIBGRAPI.2011.21",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2011.21",
language = "en",
ibi = "8JMKD3MGPBW34M/3A32SLE",
url = "http://urlib.net/ibi/8JMKD3MGPBW34M/3A32SLE",
targetfile = "86589.pdf",
urlaccessdate = "2024, Apr. 30"
}