This study uses self-organizing feature maps to model the acquisition of lexical and grammatical aspect. Previous research has identified a strong association between lexical aspect and grammatical aspect in child language, on the basis of which some researchers proposed innate semantic categories (Bickerton, 1984) or prelinguistic semantic space (Slobin, 1985). Our simulations indicate that th...

A holistic system for the recognition of handwritten Farsi/Arabic words using right}left discrete hidden Markov models (HMM) and Kohonen self-organizing vector quantization is presented. The histogram of chain-code directions of the image strips, scanned from right to left by a sliding window, is used as feature vectors. The neighborhood information preserved in the self-organizing feature map ...

A new system to segment and label CTrMRI brain slices using feature extraction and unsupervised clustering is Ž . presented. Each volume element voxel is assigned a feature pattern consisting of a scaled family of differential geometrical invariant features. The invariant feature pattern is then assigned to a specific region using a two-stage neural network Ž . system. The first stage is a self...

A new system to segment and label CT/MRI brain slices using feature extraction and unsupervised clustering is presented. Each volume element (voxel) is assigned a feature pattern consisting of a scaled family of diierential geometrical invariant features. The invariant feature pattern is then assigned to a speciic region using a two-stage neural network system. The rst stage is a self-organizin...

A new system to segment and label CT/MRI brain slices using feature extraction and unsupervised clustering is presented. Each volume element (voxel) is assigned a feature pattern consisting of a scaled family of diierential geometrical invariant features. The invariant feature pattern is then assigned to a speciic region using a two-stage neural network system. The rst stage is a self-organizin...

Bioinformatics has recently drawn a lot of attention to efficiently analyze biological genomic information with information technology, especially pattern recognition. In this paper, we attempt to explore extensive features and classifiers through a comparative study of the most promising feature selection methods and machine learning classifiers. The gene information from a patient’s marrow ex...

SRM drives are the upcoming drives nowadays as these have many advantages such as simplicity , low manufacturing and operating costs, fault tolerance, high torque/inertia ratio and efficiency. The estimation of SRM drive parameters is an important consideration in their field. Many methods are available for this. However the estimation of the optimal parameters is normally preferred. Making use...

A number of applications of self organizing feature maps require a powerful hardware. The algorithm of SOFMs contains multiplications, which need a large chip area for fast implementation in hardware. In this paper a resticted class of self organizing feature maps is investigated. Hardware aspects are the fundamental ideas for the restictions, so that the necessary chip area for each processor ...

We present a method for an automated quality control of textile seams, which is aimed to establish a standardized quality measure and to lower costs in manufacturing. The system consists of a suitable image acquisition setup, an algorithm for locating the seam, a feature extraction stage and a neural network of the self-organizing map type for feature classification. A procedure to select an op...

In this paper, it is shown that the Feature-Extracting Bidirectional Associative Memory (FEBAM) can encompass competitive model features based on winner-take-all, kwinners-take-all and self-organizing feature map properties. The modified model achieves perceptual multidimensional feature extraction, cluster-based category formation through simultaneous creation of prototype/exemplar memories, a...