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...

This paper presents the development of soft clustering and learning vector quantization (LVQ) algorithms that rely on multiple weighted norms to measure the distance between the feature vectors and their prototypes. Clustering and LVQ are formulated in this paper as the minimization of a reformulation function that employs distinct weighted norms to measure the distance between each of the prot...

---------------------------------------------------------------------***--------------------------------------------------------------------Abstract In this work a multilingual speaker identification system is proposed. The feature extraction techniques employed in system extract Mel frequency cepstral coefficient (MFCC), delta mel frequency cepstral coefficient (DMFCC) and format frequency. Th...

We applied a method called Distinction-Sensitive Learning Vector Quantization (DSLVQ) to the classification of footsteps. The measurements were made by a pressure-sensitive floor, which is part of the smart sensing living room in our research laboratory. The aim is to identify walkers based on their single footsteps. DSLVQ is an extended version of Learning Vector Quantization (LVQ), and it can...