Palette re-ordering is a well known and very effective approach for improving the compression of color-indexed images. If the spatial distribution of the indexes in the image is smooth, greater compression ratios may be obtained. As known, obtaining an optimal re-indexing scheme is not a simple problem. In this paper we provide a novel algorithm for palette re-ordering problem showing the advan...

Obtaining classification systems with both good generalization performance and high recognition rates often requires careful study on methods of combining multiple classifiers. In this paper, a Kohonen self-organization neural network based combination method is proposed. The system uses the Kohonen's model to integrate the results of several neural network classifiers. The theoretical analysis...

In this paper, we propose an approach that combines the unsupervised and supervised learning techniques for unconstrained handwritten numeral recognition. This approach uses the Kohonen self-organizing neural network for data classification in the first stage and the learning vector quantization (LVQ) model in the second stage to improve classification accuracy. The combined architecture perfor...

In this paper, we present a neural approach for unsupervised natural color-texture image segmentation, which is based on both Kohonen maps and mathematical morphology, using a combination of the texture and the image color information of the image, namely, the fractal features based on fractal dimension are selected to present the information texture, and the color features presented in RGB col...

In this paper we present an experiment of the visuomotor coordination of a simple mobile robot. Our approach to sensorimotor modelling belongs to the category of learning by doing. First, < perception, action > pairs are collected by observing the robot behavior during operation. Then, a learning by examples method is used to estimate the parameters of the model. In our experiment the learning ...

The amount of collected and stored information is growing every day in several areas of knowledge and data mining techniques are applied to these datasets in order to extract useful knowledge. One or another algorithm, or the same algorithm with different attributes, can lead to different results due to the dataset diversity. To solve this problem, machines committees methods were developed. A ...

The Self-Organizing Map (SOM) is an unsupervised neural network introduced in the 80’s by Teuvo Kohonen. In this paper, we propose a method of simultaneously using two kinds of SOM whose features are different (the nSOM method). Namely, one is distributed in the area at which input data are concentrated, and the other self-organizes the whole of the input space. The competing behavior of the tw...

We calculate analytically the magnification behaviour of a generalized family of self-organizing feature maps inspired by a variant introduced by Kohonen in 1991, denoted here as Winner Relaxing Kohonen algorithm, which is shown here to have a magnification exponent of 4/7. Motivated by the observation that a modification of the learning rule for the winner neuron influences the magnification l...

This section concerns neural networks which are hybrid either in terms of structure or in terms of training algorithms. The counterpropagation network is one that incorporates structural characteristics of the Kohonen and Grossberg networks and it is trained by composite supervised–unsupervised methods. The adaptive critic concept concerns neural network implementations of reinforcement learnin...

This paper analyses the results obtained when different topologies of 1-Dimensional Kohonen Networks where used to classify color pictures taken to Popocatépetl Volcano (located in the State of Puebla, México, active and monitored since 1997). Due to the fact that volcano images needed to be correlated with other experiments in the research center where this was carried out, it was required to ...