In this paper, the segmentation of ultrasonic images using self-organizing neural networks (NN) is investigated. A modification of Learning Vector Quantizer (called L2 LVQ) is proposed so that the weight vectors of the output neurons correspond to the L2 mean instead of the sample arithmetic mean of the input observations. The convergence in the mean and in the mean square of the proposed varia...

A novel class of Learning Vector Quantizers LVQs based on multivariate order statistics is proposed in order to overcome the drawback that the estima tors for obtaining the reference vectors in LVQ do not have robustness either against erroneous choices for the winner vector or against the outliers that may exist in vector valued observations The per formance of the proposed variants of LVQ is ...

An adaptive algorithm for training of a Nearest Neighbour (NN) classifier is developed in this paper. This learning rule has got some similarity to the well-known LVQ method, but using the nearest centroid neighbourhood concept to estimate optimal locations of the codebook vectors. The aim of this approach is to improve the performance of the standard LVQ algorithms when using a very small code...

The nearest neighbor method together with the dynamic time warping (DTW) distance is one of the most popular approaches in time series classification. This method suffers from high storage and computation requirements for large training sets. As a solution to both drawbacks, this article extends learning vector quantization (LVQ) from Euclidean spaces to DTW spaces. The proposed LVQ scheme uses...

Similarity-based methods belong to the most accurate data mining approaches. A large group of such methods is based on instance selection and optimization, with Learning Vector Quantization (LVQ) algorithm being a prominent example. Accuracy of LVQ highly depends on proper initialization of prototypes and the optimization mechanism. Prototype initialization based on context dependent clustering...

In this paper, we show that the LVQ learning algorithm converges to locally asymptotic stable equilibria of an ordinary differential equation. We show that the learning algorithm performs stochastic approximation. Convergence of the Voronoi vectors is guaranteed under the appropriate conditions on the underlying statistics of the classification problem. We also present a modification to the lea...

This paper presents a new method to detect high impedance faults in radial distribution systems. Magnitudes of third and fifth harmonic components of voltages and currents are used as a feature vector for fault discrimination. The proposed methodology uses a learning vector quantization (LVQ) neural network as a classifier for identifying high impedance arc-type faults. The network learns from ...

Automatic text classification is an important task for many natural language processing applications. This paper presents a neural approach to develop a text classifier based on the Learning Vector Quantization (LVQ) algorithm. The LVQ model is a classification method that uses a competitive supervised learning algorithm. The proposed method has been applied to two specific tasks: text categori...

In this article we consider a median variant of the learning vector quantization (LVQ) classifier for classification of dissimilarity data. However, beside the median aspect, we propose to optimize the receiver-operating characteristics (ROC) instead of the classification accuracy. In particular, we present a probabilistic LVQ model with an adaptation scheme based on a generalized ExpectationMa...

Many pattern analysis problems require classification of examples into naturally ordered classes. In such cases, nominal classification schemes will ignore the class order relationships, which can have a detrimental effect on classification accuracy. This article introduces two novel ordinal learning vector quantization (LVQ) schemes, with metric learning, specifically designed for classifying ...