Prototype based classifiers so far can only work with hard labels on the training data. In order to allow for soft labels as input label and answer, we enhanced the original LVQ algorithm. The key idea is adapting the prototypes depending on the similarity of their fuzzy labels to the ones of training samples. In experiments, the performance of the fuzzy LVQ was compared against the original ap...

An analog VLSI architecture for learning vector quantization (LVQ), with on-chip adaptation and dynamic storage of the analog templates, is presented. The architecture extends to Fuzzy ART and Kohonen self-organizing maps through digital programming. The analog memory and adaptive element of the LVQ cell comprise 6 MOS transistors and one capacitor, and provide for robust selfrefresh of the dyn...

This paper can be seen from two sides. From the first side as the answer of the question: how to initialize the Learning Vectors Quantization algorithm. And from second side it can be seen as the method of improving of instances selection algorithms. In the article we propose to use a conjunction of the LVQ and some of instances selection algorithms because it simplify the LVQ initialization an...

Learning Vector Quantisation (LVQ) is a method of applying the Vector Quantisation (VQ) to generate references for Nearest Neighbour (NN) classification. Though successful in many occasions, LVQ suffers from several shortcomings, especially the reference vectors are prone to diverge. In this paper, we propose a Classified Vector Quantisation (CVQ) to establish VQ for classification. By CVQ, eac...

A general technique is proposed for embedding online clustering algorithms based on competitive learning in a reinforcement learning framework. The basic idea is that the clustering system can be viewed as a reinforcement learning system that learns through reinforcements to follow the clustering strategy we wish to implement. In this sense, the reinforcement guided competitive learning (RGCL) ...

In this note we formulate image segmentation as a clustering problem. Feature vectors, extracted from a raw image are clustered into subregions, thereby segmenting the image. A fuzzy generalization of Kohonen learning vector quantization (LVQ) which integrates the Fuzzy cMeans (FCM) model with the learning rate and updating strategies of the LVQ Is used for this task. This network, which segmen...

We propose in this contribution a method for l1-regularization in prototype based relevance learning vector quantization (LVQ) for sparse relevance profiles. Sparse relevance profiles in hyperspectral data analysis fade down those spectral bands which are not necessary for classification. In particular, we consider the sparsity in the relevance profile enforced by LASSO optimization. The latter...

In this note we formulate image segmentation as a clustering problem. Feature vectors, extracted from a raw image are clustered into subregions, thereby segmenting the image. A fuzzy generalization of Kohonen learning vector quantization (LVQ) which integrates the Fuzzy cMeans (FCM) model with the learning rate and updating strategies of the LVQ Is used for this task. This network, which segmen...