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, 
                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 
  conference-location = "Macei{\'o}",
      conference-year = "Aug. 28 - 31, 2011",
             language = "en",
           targetfile = "86589.pdf",
        urlaccessdate = "2020, Feb. 25"