Land-use classification was performed by using a set of ERS-1, JERSand Radarsat images. Classes were water, forests (with subclasses according to stem volume), agricultural field, mire and urban area. Median filtering was used for speckle reduction and principal component analysis for feature extraction. Spectral classification was performed by using self-organizing feature map and learning vec...

Obtaining semantic or functional word categories from data in an unsupervised manner is a problem motivated both from the linguistic point of view and from that of construing language models for various language processing tasks. In this work, we use the self-organizing map algorithm to visualize and cluster common Finnish verbs based on functional and semantic information coded by case marking...

Introduction Geographic data are often very large in volume and “characterized by a high number of attributes or dimensions” [1]. There are urgent needs to develop effective and yet efficient approaches for analyzing such voluminous and high-dimensional data to address complex geographic problems [1, 2, 3, 4], e.g., detecting unknown multivariate patterns or relationships between socioeconomic,...

Self-organizing mapping is an unsupervised learning paradigm used in pattern classification and hence artificial intelligence. This paradigm is based on modifying the class features via the incoming input stimuli. Its exciting part is that it introduces concepts such as neighborhood or mapping. Hence the results obtained from this paradigm highly depend on the selected neighborhood and mapping ...

Automatic detection and correction of image orientation is of great importance in intelligent image processing. In this paper, we present an automatic image orientation detection algorithm based on the supervised selforganizing map (SOM). The SOM is trained by using compact and efficient low-level chrominance (color) features in a supervised manner. Experiments have been conducted on a database...

Feature selective cells in the primary visual cortex of several species are organized in hierarchical topographic maps of stimulus features like "position in visual space", "orientation" and" ocular dominance". In order to understand and describe their spatial structure and their development, we investigate a self-organizing neural network model based on the feature map algorithm. The model exp...

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...