Recently, an extension of popular learning vector quantization (LVQ) to general dissimilarity data has been proposed, relational generalized LVQ (RGLVQ) [10, 9]. An intuitive prototype based classification scheme results which can divide data characterized by pairwise dissimilarities into priorly given categories. However, the technique relies on the full dissimilarity matrix and, thus, has squ...

Usually, objects to be classified are represented by features. In this paper, we discuss an alternative object representation based on dissimilarity values. If such distances separate the classes well, the nearest neighbor method offers a good solution. However, dissimilarities used in practice are usually far from ideal and the performance of the nearest neighbor rule suffers from its sensitiv...

In machine learning the Fuzzy c-Means algorithm (FCM) plays an important role. This prototype based unsupervised clustering method has been extensively studied and applied to a great variety of problems from different research areas like medicine and biology. Commonly the Euclidean distance is used as dissimilarity measure, although any dissimilarity measure would be suited. Recently divergence...

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...

A crucial issue in dissimilarity-based classification is the choice of the representation set. In the small sample case, classifiers capable of a good generalization and the injection or addition of extra information allow to overcome the representational limitations. In this paper, we present a new approach for enriching dissimilarity representations. It is based on the concept of feature line...

The most widely used measure of segregation is the dissimilarity index, D. It is now well understood that this measure also reflects randomness in the allocation of individuals to units; that is, it measures deviations from evenness not deviations from randomness. This leads to potentially large values of the segregation index when unit sizes and/or minority proportions are small, even if there...

Prototype based models offer an intuitive interface to given data sets by means of an inspection of the model prototypes. Supervised classification can be achieved by popular techniques such as learning vector quantization (LVQ) and extensions derived from cost functions such as generalized LVQ (GLVQ) and robust soft LVQ (RSLVQ). These methods, however, are restricted to Euclidean vectors and t...