Results 241 to 250 of about 57,535 (295)

Soft Learning Vector Quantization

Neural Computation, 2003
Learning vector quantization (LVQ) is a popular class of adaptive nearest prototype classifiers for multiclass classification, but learning algorithms from this family have so far been proposed on heuristic grounds. Here, we take a more principled approach and derive two variants of LVQ using a gaussian mixture ansatz. We propose an objective function
Klaus Obermayer, Sambu Seo
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Generalized relevance learning vector quantization

Neural Networks, 2002
We propose a new scheme for enlarging generalized learning vector quantization (GLVQ) with weighting factors for the input dimensions. The factors allow an appropriate scaling of the input dimensions according to their relevance. They are adapted automatically during training according to the specific classification task whereby training can be ...
Hammer, Barbara, Villmann, Th.
openaire   +5 more sources

A dynamic approach to learning vector quantization

Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004
Learning vector quantization networks are generally considered a powerful pattern recognition tool. Their main drawback, however, is the competitive learning algorithm they are based upon, that suffers of the so called underutilized or dead unit problem.
DE STEFANO, Claudio, D'ELIA, Ciro, MARCELLI, Angelo   +2 more
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Expansive and Competitive Learning for Vector Quantization [PDF]

Neural Processing Letters, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
José Muñoz-Pérez, Jose Antonio Gomez-Ruiz, Ezequiel López-Rubio, M. A. García-Bernal   +3 more
openaire   +1 more source

Fuzzy learning vector quantization

Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan), 2005
In this paper, a new supervised competitive learning network model called fuzzy learning vector quantization (FLVQ) which incorporates fuzzy concepts into the learning vector quantization (LVQ) networks is proposed. Unlike the original algorithm, the FLVQ's learning algorithm is derived from optimizing an appropriate fuzzy objective function which ...
Fu-Lai Chung, Tong Lee
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EEG Classification by Learning Vector Quantization - EEG-Klassifikation mit Hilfe eines Learning Vector Quantizers [PDF]

Biomedizinische Technik/Biomedical Engineering, 1992
EEG classification using Learning Vector Quantization (LVQ) is introduced on the basis of a Brain-Computer Interface (BCI) built in Graz, where a subject controlled a cursor in one dimension on a monitor using potentials recorded from the intact scalp.
Doris Flotzinger, J. Kalcher, Gert Pfurtscheller   +2 more
openaire   +2 more sources