Evolution of Multi-class Single Layer Perceptron

  • Sarunas Raudys
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4432)

Abstract

While training single layer perceptron (SLP) in two-class situation, one may obtain seven types of statistical classifiers including minimum empirical error and support vector (SV) classifiers. Unfortunately, both classifiers cannot be obtained automatically in multi-category case. We suggest designing K(K-1)/2 pair-wise SLPs and combine them in a special way. Experiments using K=24 class chromosome and K=10 class yeast infection data illustrate effectiveness of new multi-class network of the single layer perceptrons.

Keywords

Decision Boundary Fusion Rule Pattern Class Training Vector Support Vector Classifier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Sarunas Raudys
    • 1
  1. 1.Vilnius Gediminas Technical University, Sauletekio 11, Vilnius, LT-10223Lithuania

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