in this paper we address the issue of recognizing farsi handwritten words. two types of gradient features are extracted from a sliding vertical stripe which sweeps across a word image. these are directional and intensity gradient features. the feature vector extracted from each stripe is then coded using the self organizing map (som). in this method each word is modeled using the discrete hidde...

abstract this paper presents a robust approach using artificial neural networks in the form of a self organizing map (som) as a semi-automatic method for analysis and identification of morphometric features in two completely different environments, the man and biosphere reserve “eastern carpathians” (central europe) in a complex mountainous humid area and yardangs in lut desert, iran, a hyper a...

Time information of the input data is used for evaluating the goodness of the Self-Organizing Map to store and represent temporal feature vector sequences. A new node neighborhood is defined for the map which takes the temporal order of the input samples into account. A connection is created between those two map nodes which are the best-matching units for two successive input samples in time. ...

We present an efficient approach to forming feature maps. The method involves three stages. In the first stage, we use the K-means algorithm to select N2 (i.e., the size of the feature map to be formed) cluster centers from a data set. Then a heuristic assignment strategy is employed to organize the N2 selected data points into an N x N neural array so as to form an initial feature map. If the ...

In this paper we demonstrate that the Self-Organizing Maps of Kohonen can be used as speech feature ex-tractors that are able to take temporal context into account. We have investigated two alternatives to use SOMs as such feature extractors, one based on tracing the location of highest activity on a SOM, the other on integrating the activity of the whole SOM for a period of time. The experimen...