This paper presents a supervised competitive learning network approach, called a fuzzy-soft learning vector quantization, for control chart pattern recognition. Unnatural patterns in control charts mean that there are some unnatural causes for variations in statistical process control (SPC). Hence, control chart pattern recognition becomes more important in SPC. In order to detect e€ ectively t...

Abstract Learning Vector Quantization (LVQ) is one of the most widely used classification approaches. LVQ faces a problem as when size data grows large it becomes slower. In this paper, modified version LVQ, which called PDLVQ proposed to accelerate traditional version. The scheme aims avoid unnecessary computations by applying an efficient Partial Distance (PD) computation strategy. Three diff...

The learning vector quantization (LVQ) algorithm is widely used in image compression because of its intuitively clear learning process and simple implementation. However, LVQ strongly depends on the initialization of the codebook and often converges to local optimal results. To address the issues, a new two-step LVQ (TsLVQ) algorithm is proposed in the paper. TsLVQ uses a correcting learning st...

This paper is the extension of two-stage vector quantization–(spherical) lattice vector quantization (VQ–(S)LVQ) recently introduced by Pan and Fischer [1]. First, according to high resolution quantization theory, generalized vector quantization–lattice vector quantization (G-VQ–LVQ) is formulated in order to release the constraint of the spherical boundary for the second-stage lattice vector q...

Learning vector quantization (LVQ) constitutes a powerful and intuitive method for adaptive nearest prototype classification. However, original LVQ has been introduced based on heuristics and numerous modifications exist to achieve better convergence and stability. Recently, a mathematical foundation by means of a cost function has been proposed which, as a limiting case, yields a learning rule...

In this correspondence, we propose a novel class of learning vector quantizers (LVQ’s) based on multivariate data ordering principles. A special case of the novel LVQ class is the median LVQ, which uses either the marginal median or the vector median as a multivariate estimator of location. The performance of the proposed marginal median LVQ in color image quantization is demonstrated by experi...

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...

We introduce a novel way to employ codebooks trained by Learning Vector Quantization together with hidden Markov models. In previous work, LVQ-codebooks have been used as frame labelers. The resulting label stream has been modeled and decoded by discrete observation HMMs. We present a way to extract more information out of the LVQ stage. This is accomplished by modeling the class-wise quantizat...

Learning vector quantization (LVQ) is a supervised neural network method applicable in non-linear separation problems and widely used for data classification. Existing LVQ algorithms are mostly focused on numerical data. This paper presents a batch type LVQ algorithm used for classifying data with categorical values. The batch learning rules make possible to construct the learning methodology f...