Lattice vector quantization (LVQ) reduces computational load and design complexity due to its regular structure. In this letter, we introduce and analyze the performance of two hybrid combinations of two lattices i.e. the AnAn and AnDn. Experiment results show that multistage LVQ with lattice AnA combination in four dimensional vector offers the least quantization errors with p = 0.0098 as comp...

This paper presents a real-time hand gesture recognizer based on a Learning Vector Quantization (LVQ) classifier. The recognizer is formed by two modules. The first module, mainly composed of a data glove, performs the feature extraction. The second module, the classifier, is performed by means of LVQ. The recognizer, tested on a dataset of 3900 hand gestures, performed by people of different g...

In this study, two kinds of neural networks are employed to develop a supervised ECG beat classifier. In order to improve the performance of the MLP classifier for application to ECG signal, the performance is compared to an LVQ neural network classifier. The two classifiers are tested with selected ECG time series and experimental results show that the MLP classifier offers a great potential i...

OF THESIS ANALYSIS OF LVQ IN THE CONTEXT OF SPONTANEOUS EEG SIGNAL CLASSIFICATION Learning Vector Quantization (LVQ) has proven to be an e ective classi cation procedure. Since its introduction by Kohonen in 1990 several extensions to the basic algorithm have been proposed. This paper investigates what and how LVQ learns in the context of EEG signal classi cation. LVQ is shown to be comparable ...

Statistical process control (SPC) is a method for improving the quality o f products. Control charting plays a most important role in SPC. SPC control charts arc used for monitoring and detecting unnatural process behaviour. Unnatural patterns in control charts indicate unnatural causes for variations. Control chart pattern recognition is therefore important in SPC. Past research shows that alt...

We propose a new matrix learning scheme to extend Generalized Relevance Learning Vector Quantization (GRLVQ). By introducing a full matrix of relevance factors in the distance measure, correlations between different features and their importance for the classification scheme can be taken into account. In comparison to the weighted euclidean metric used for GRLVQ, this metric is more powerful to...

Unnatural control chart patterns (CCPs) are associated with a particular set of assignable causes for process variation. Hence, effectively recognizing CCPs can substantially narrow down the set of possible causes to be examined, and accelerate the diagnostic search. Recently, machine-learning techniques, especially the artificial neural network (ANN), have been widely used as an effective tool...

Educational games more interesting if given an artificial intelligence. One of the intelligence algorithms that can be applied to this game is Learning Vector Quantization (LVQ). LVQ intelligent algorithm implemented in because produce desired classification. In study, researcher created educational for learning Arabic Vocabulary using algorithm. This used determine level when a player playing ...

This paper shows that the competitive learning rule found in Learning Vector Quantization (LVQ) serves as a promising function approximator to enable reinforcement learning methods to cope with a large decision search space, defined in terms of different classes of input patterns, like those found in the game of Go. In particular, this paper describes S[arsa]LVQ, a novel reinforcement learning ...

This paper presents a Learning Vector Quantization (LVQ)-based temporal tracking method for semi-automatic video object segmentation. A semantic video object is initialized using user assistance in a reference frame to give initial classification of video object and its background regions. The LVQ training approximates video object and background classification and use them for automatic segmen...