… 20 years old Learning Vector Quantization (LVQ) algorithm … Although LVQ was designed
as an approximation to nearest … In section 5 we show how LVQ family of algorithms emerges …

Abstract: Learning Vector Quantization (LVQ) is a group … LVQ PAK program package contains
all programs necessary for the correct application of certain Learning Vector Quantization …

… We study LVQ rigorously within a simplifying model situation: two competing prototypes are
… LVQ+/-, a limiting case of LVQ2.1. The former shows close to optimal performance, while LVQ…

… variants of LVQ. We study five LVQ algorithms in detail: Kohonen’s original LVQ1, unsupervised
vector quantization (VQ), a mixture of VQ and LVQ, LVQ2.1, and a variant of LVQ which …

… In this study, LVQ algorithm has been used for training the data classification. All layers are
not fully connected in this study due to the structure of LVQ although many of studies in the …

… Besides, the nonlinear fitting ability of LVQ neural network is also very strong. LVQ is a …
In view of the advantages of LVQ neural network, we attempt to use LVQ neural network to …

… This paper is an overview of the program package LVQ PAK, which has been developed
for convenient and e ective application of Learning Vector Quantization algorithms. Two new …

We propose a new matrix learning scheme to extend Generalized Relevance Learning
Vector Quantization (GRLVQ). By introducing a full matrix of relevance factors in the distance …

This paper describes the application of competitive neural networks with the LVQ algorithm
for classification of electrocardiogram signals (ECG). For this study we used the MIT–BIH …

… training paradigm: prototype based learning vector quantization (LVQ) as proposed by
Kohonen [11]. We discuss a recent extension of simple LVQ which integrates an adaptive metric, …