Generalized derivative based kernelized learning vector quantization

Authors:
Frank-Michael Schleif;Thomas Villmann;Barbara Hammer;Petra Schneider;Michael Biehl
Affiliations:
Dept. of Techn., Univ. of Bielefeld, Bielefeld, Germany;Faculty of Math./Natural and CS, Univ. of Appl. Sc. Mittweida, Mittweida, Germany;Dept. of Techn., Univ. of Bielefeld, Bielefeld, Germany;Johann Bernoulli Inst. for Math. and CS, Univ. of Groningen, Groningen, The Netherlands;Johann Bernoulli Inst. for Math. and CS, Univ. of Groningen, Groningen, The Netherlands
Venue:
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
Year:
2010

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Abstract

We derive a novel derivative based version of kernelized Generalized Learning Vector Quantization (KGLVQ) as an effective, easy to interpret, prototype based and kernelized classifier. It is called D-KGLVQ and we provide generalization error bounds, experimental results on real world data, showing that D-KGLVQ is competitive with KGLVQ and the SVM on UCI data and additionally show that automatic parameter adaptation for the used kernels simplifies the learning.