SUMMARY The Learning Vector Quantization (LVQ) Neural Network approach has been widely used in acoustic seafloor classification. However, one of its major weak points is the sensitivity to the initialization, affecting the seafloor classification accuracy. In this paper, Genetic Algorithm (GA) is used to optimize the initial values of LVQ. The GA-based LVQ can rapidly provide the optimum initia...

The program package is available at the Internet site "cochlea.hut." and will be updated continuously. The indexing of the latest release will always be of the form "lvq pak-X.Y". The instructions below are indexed as in the version lvq pak-1.1, which was released on December 31, 1991. The programs are available in two archive formats, one for the UNIX-environment, the other for MS-DOS, respect...

BACKGROUND Mouth breathing is a chronic syndrome that may bring about postural changes. Finding characteristic patterns of changes occurring in the complex musculoskeletal system of mouth-breathing children has been a challenge. Learning vector quantization (LVQ) is an artificial neural network model that can be applied for this purpose. OBJECTIVES The aim of the present study was to apply LV...

Modified nucleosides were recently presented as potential tumor markers for breast cancer. The patterns of the levels of urinary nucleosides are different for tumor bearing individuals and for healthy individuals. Thus, a powerful pattern recognition method is needed. Although backpropagation (BP) neural networks are becoming increasingly common in medical literature for pattern recognition, it...

Discriminative vector quantization schemes such as learning vector quantization (LVQ) and extensions thereof offer efficient and intuitive classifiers which are based on the representation of classes by prototypes. The original methods, however, rely on the Euclidean distance corresponding to the assumption that the data can be represented by isotropic clusters. For this reason, extensions of t...

Discriminative vector quantization schemes such as learning vector quantization (LVQ) and extensions thereof offer efficient and intuitive classifiers based on the representation of classes by prototypes. The original methods, however, rely on the Euclidean distance corresponding to the assumption that the data can be represented by isotropic clusters. For this reason, extensions of the methods...

A variety of modifications have been employed to learning vector quantization (LVQ) algorithms using either crisp or soft windows for selection of data. Although these schemes have been shown in practice to improve performance, a theoretical study on the influence of windows has so far been limited. Here we rigorously analyze the influence of windows in a controlled environment of gaussian mixt...

This paper presents a study investigating the potential of artiicial neural networks (ANN's) for the classiication and segmentation of magnetic resonance (MR) images of the human brain. In this study, we present the application of a Learning Vector Quantization (LVQ) Artiicial Neural Network (ANN) for the multispectral supervised classiication of MR images. We have modiied the LVQ for better an...

The issue of Automatic Relevance Determination (ARD) has attracted attention over the last decade for the sake of efficiency and accuracy of classifiers, and also to extract knowledge from discriminant functions adapted to a given data set. Based on Learning Vector Quantization (LVQ), we recently proposed an approach to ARD utilizing genetic algorithms. Another approach is the Generalized Relev...