This paper presents an unsupervised fuzzy-kernel learning vector quantization algorithm called FKLVQ. FKLVQ is a batch type of clustering learning network by fusing the batch learning, fuzzy membership functions, and kernel-induced distance measures. We compare FKLVQ with the wellknown fuzzy LVQ and the recently proposed fuzzy-soft LVQ on some artificial and real data sets. Experimental results...

We propose to use learning vector quantization (LVQ) in novelty detection where a few outliers exist in training data. The codebook update of original LVQ is modified and the scheme to determine a threshold for each codebook is proposed. Experimental results on artificial and real-world problems are quite promising.

In this paper, we propose to use learning vector quantization for the efficient partitioning of a cooccurrence space. A simple codebook is trained to map the multidimensional cooccurrence space into a 1-dimensional cooccurrence histogram. In the classification phase a nonparametric log-likelihood statistic is employed for comparing sample and prototype histograms. The advantages of vector quant...

In this paper we introduce an integrative approach towards color texture classification learned by a supervised framework. Our approach is based on the Generalized Learning Vector Quantization (GLVQ), extended by an adaptive distance measure which is defined in the Fourier domain and 2D Gabor filters. We evaluate the proposed technique on a set of color texture images and compare results with t...

We apply Learning Vector Quantization (LVQ) in automated boar semen quality assessment. The classification of single boar sperm heads into healthy (normal) and non-normal ones is based on grey-scale microscopic images only. Sample data was classified by veterinary experts and is used for training a system with a number of prototypes for each class. We apply as training schemes Kohonen’s LVQ1 an...

The paper deals with the concept of relevance learning in learning vector quantization and classification. Recent machine learning approaches with the ability of metric adaptation but based on different concepts are considered in comparison to variants of relevance learning vector quantization. We compare these methods with respect to their theoretical motivation and we demonstrate the differen...

In this paper the effectiveness of a corrective learning algorithm MIL (Mirror Image Learning) [1], [2] is comparatively studied with that of GLVQ (Generalized Learning Vector Quantization) [3]. Both MIL and GLVQ were proposed to improve the learning effectiveness beyond the limitation due to independent estimation of class conditional distributions. While the GLVQ modifies the representative v...

In this article, we extend a local prototype-based learning model by active learning, which gives the learner the capability to select training samples and thereby increase speed and accuracy of the model. Our algorithm is based on the idea of selecting a query on the borderline of the actual classification. This can be done by considering margins in an extension of learning vector quantization...

In this contribution, prototype-based systems and relevance learning are presented and discussed in the context of biomedical data analysis. Learning Vector Quantization and Matrix Relevance Learning serve as the main examples. After introducing basic concepts and related approaches, example applications of Generalized Matrix Relevance Learning are reviewed, including the classification of adre...

In a recent paper, Lee and Neuhoff found an asymptotic formula for the distribution of the length of the errors produced by a vector quantizer with many quantization points. This distribution depends on the source probability density, the quantizer point density and the quantizer shape profile. (The latter characterizes the shapes of the quantization cells as a function of position.) The purpos...