Journal article

Publication year: 2005

Pages: 21-44

Journal: Neural Processing Letters

Volume number: 21

Issue number: 1

ISSN: 1370-4621

eISSN: 1573-773X

DOI Link: https://doi.org/10.1007/s11063-004-3255-2

Publisher: Springer

Abstract: 
Prototype based classification offers intuitive and sparse models with excellent generalization ability. However, these models usually crucially depend on the underlying Euclidian metric; moreover, online variants likely suffer from the problem of local optima. We here propose a generalization of learning vector quantization with three additional features: (I) it directly integrates neighborhood cooperation, hence is less affected by local optima; (II) the method can be combined with any differentiable similarity measure whereby metric parameters such as relevance factors of the input dimensions can automatically be adapted according to the given data; (III) it obeys a gradient dynamics hence shows very robust behavior, and the chosen objective is related to margin optimization.

Harvard Citation style: Hammer, B., Strickert, M. and Villmann, T. (2005) Supervised neural gas with general similarity measure, Neural Processing Letters, 21(1), pp. 21-44. https://doi.org/10.1007/s11063-004-3255-2

APA Citation style: Hammer, B., Strickert, M., & Villmann, T. (2005). Supervised neural gas with general similarity measure. Neural Processing Letters. 21(1), 21-44. https://doi.org/10.1007/s11063-004-3255-2