Generalized Derivative Based Kernelized Learning Vector Quantization

Frank-Michael Schleif, Thomas Villmann, Barbara Hammer, Petra Schneider, Michael Biehl

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

11 Citations (Scopus)
290 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationProc. Intelligent Data Engineering and Automated Learning - IDEAL 2010
EditorsColin Fyfe, Peter Tino, Darryl Charles, Cesar Garcia-Osoro, Hujun Yin
Number of pages8
Publication statusPublished - 2010

Publication series

NameLecture Notes in Computer Science

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