caa a22 4500 60546880X CHVBK 20210128100318.0 cr unu---uuuuu 210128e20150901xx s 000 0 eng 10.1007/s00500-014-1496-1 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1496-1 Border-sensitive learning in generalized learning vector quantization: an alternative to support vector machines [Elektronische Daten] [Marika Kaden, Martin Riedel, Wieland Hermann, Thomas Villmann] Learning vector quantization (LVQ) algorithms as powerful classifier models for class discrimination of vectorial data belong to the family of prototype-based classifiers with a learning scheme based on Hebbian learning as a widely accepted neuronal learning paradigm. Those classifier approaches estimate the class distribution and generate from this a class decision for vectors to be classified. The estimation can be done by the determination of class-typical sensitive prototypes inside the class distribution area like in LVQ or by detection of the class borders for class discrimination as preferred by support vector machines (SVMs). Both strategies provide advantages and disadvantages depending on the given classification task. Whereas LVQs are very intuitive and usually process the data during learning in the data space, frequently equipped with variants of the Euclidean metric, SVMs implicitly map the data into a high-dimensional kernel-induced feature space for better separation. In this Hilbert space, the inner product is compliant to the kernel. However, this implicit mapping makes a vivid interpretation more difficult. As an alternative, we propose in this paper two modifications of LVQ to make it comparable to SVM: first border-sensitive learning is introduced to achieve border-responsible prototypes comparable with support vectors in SVM. Second, kernel distances for differentiable kernels are considered, such that prototype learning takes place in a metric space isomorphic to the feature mapping space of SVM. Combination of both features gives a powerful prototype-based classifier while keeping the easy interpretation and the intuitive Hebbian learning scheme of LVQ. Springer-Verlag Berlin Heidelberg, 2014 Learning vector quantization nationallicence Border sensitive classification nationallicence Kernel distance nationallicence Support vector machines nationallicence Kaden Marika Computational Intelligence Group, University of Applied Sciences Mittweida, Mittweida, Germany aut Riedel Martin Computational Intelligence Group, University of Applied Sciences Mittweida, Mittweida, Germany aut Hermann Wieland Department of Neurology, Paracelsus Hospital Zwickau, Zwickau, Germany aut Villmann Thomas Computational Intelligence Group, University of Applied Sciences Mittweida, Mittweida, Germany aut Soft Computing Springer Berlin Heidelberg 19/9(2015-09-01), 2423-2434 1432-7643 19:9<2423 2015 19 500 https://doi.org/10.1007/s00500-014-1496-1 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1496-1 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Kaden Marika Computational Intelligence Group, University of Applied Sciences Mittweida, Mittweida, Germany aut NATIONALLICENCE 700 1- Riedel Martin Computational Intelligence Group, University of Applied Sciences Mittweida, Mittweida, Germany aut NATIONALLICENCE 700 1- Hermann Wieland Department of Neurology, Paracelsus Hospital Zwickau, Zwickau, Germany aut NATIONALLICENCE 700 1- Villmann Thomas Computational Intelligence Group, University of Applied Sciences Mittweida, Mittweida, Germany aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/9(2015-09-01), 2423-2434 1432-7643 19:9<2423 2015 19 500