Person identification technology has many applications. It has been shown in previous studies that the brain-wave pattern of every individual is unique and that the electroencephalogram (EEG) can be used for person identification. In this paper, a kind of event related potential-P300, is employed as the input of the identification system. Compared with the other EEG signal, the P300 wave is eas...

Due to its intuitive learning algorithms and classification behavior, learning vector quantization (LVQ) enjoys a wide popularity in diverse application domains. In recent years, the classical heuristic schemes have been accompanied by variants which can be motivated by a statistical framework such as robust soft LVQ (RSLVQ). In its original form, LVQ and RSLVQ can be applied to vectorial data ...

The statistical physics analysis of offline learning is applied to cost function based learning vector quantization (LVQ) schemes. Typical learning behavior is obtained from a model with data drawn from high dimensional Gaussian mixtures and a system of two or three competing prototypes. The analytic approach becomes exact in the limit of high training temperature. We study two cost function re...

Relevance Learning Vector Quantization (RLVQ) (introduced in [1]) is a variation of Learning Vector Quantization (LVQ) which allows a heuristic determination of relevance factors for the input dimensions. The method is based on Hebbian learning and defines weighting factors of the input dimensions which are automatically adapted to the specific problem. These relevance factors increase the over...

A high-performance biologically-inspired odor identification system is described. As a means of odor recognition, learning vector quantization (LVQ) algorithm is employed. Performance improvement is obtained with the use of a preprocessing with discriminant analysis of input samples. Due to sample-based decision, the system can be reliably operated as a real-time electronic nose.

This study presents an automatic on-line adaptation mechanism to the handwriting style of a writer for the recognition of isolated handwritten characters. The classifier we use here is based on a Fuzzy Inference System (FIS) similar to those we have designed for handwriting recognition. In this FIS each premise rule is composed of a fuzzy prototype which represents intrinsic properties of a cla...

Generalized Relevance Learning Vector Quantization (GRLVQ) is combined with correlation-based similarity measures. These are derived from the Pearson correlation coefficient in order to replace the adaptive squared Euclidean distance which is typically used for GRLVQ. Patterns can thus be used without further preprocessing and compared in a manner invariant to data shifting and scaling transfor...

Lately the topic of rejecting decisions in a classification scenario became attention, e. g. in medical data analysis, since not only the decision itself but also the certainty of the decision is important. While often a reject option is used on top of a trained model, recent approaches include it directly in the objective function of the desired model, e. g. for learning vector quantization. F...

In the domain of classification tasks, artificial neural nets (ANNs) are prominent data mining methods. Paradigms like learning vector quantization (LVQ) and probabilistic neural net (PNN) are suitable classifiers. In this paper, new approaches of evolutionary optimized LVQs and PNNs are proposed. Their classification accuracy is compared with results of standard PNN and LVQ. The complex real-w...

The contribution describes our application to the ESANN'2013 Competition on Human Activity Recognition (HAR) using Android-OS smartphone sensor signals. We applied a kernel variant of learning vector quantization with metric adaptation using only one prototype vector per class. This sparse model obtains very good accuracies and additionally provides class correlation information. Further, the m...