This paper examines the opportunity of Kohonen's feature map adaptation for selection of useful features in the task of clusterization of multidimensional data. Based on the biological prototype of the self-organizing map, the modified Kohonen’s map was built in the way to be able to select useful features in the task of clusterization. The neuron map based on the new training algorithm has sho...

the purpose of this paper is organizing, classifying and visualizing the literature of supply chain coordination with mind map method. this method uses all the key skills of brain such as word, image, number, logic, rhythm and color for creating, visualizing, organizing and classifying of ideas. furthermore, it is a good utility for studying and organizing information, solving problems, making ...

This paper describes a speaker recognition model using TwoDimensional Mel-Cepstrum and predictive neural network. The speaker model consists of two networks. The rst one is a self-organizing VQ map(Kohonen's feature map). The second part is the predictive network and learns transitional patterns on the feature map of each speaker's model. TDMC consists of averaged features and dynamic features ...

In recent years a great amount of research has focused on algorithms that learn features from unlabeled data. In this work we propose a model based on the Self-Organizing Map (SOM) neural network to learn features useful for the problem of automatic natural images classification. In particular we use the SOM model to learn single-layer features from the extremely challenging CIFAR-10 dataset, c...

In this paper, we propose an approach for corporate decision making with self-organizing patent maps labeled by technical terms and AHP. First, we select the patent area of interest and collect pertinent patent documents in text format. Second, we extract keywords by text mining to transform patent documents into feature vectors of the companies. Third, we input the feature matrix of technical ...

Digital music distribution industry has seen a tremendous growth in resent years. Tasks such us automatic music genre discrimination address new and exciting research challenges. Automatic music genre recognition involves issues like feature extraction and development of classifiers using the obtained features. As for feature extraction, we base on Self-Organizing Maps to map the high-dimension...