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In the present paper we describe a recent approach of probabilistic self-organizing maps (PRSOM). The PRSOM become more and more interesting in many fields such as: pattern recognition, clustering, classification, speech recognition, data compression, medical diagnosis… The PRSOM give an estimation of the density probability function of the data, this density dependent on the parameters of the PRSOM,...
Classification experiments are made with neural network algorithm and support vector machine method separately. The samples are divided into three groups and two kinds of support vector machines based on polynomial kernel and radial basis function are applied by changing the parameter values. The simulated results show that, as for the dataset with less training samples, using simple structure learning...
Neural networks have found profound success in the area of pattern recognition. By repeatedly showing a neural network inputs classified into groups, the network can be trained to discern the criteria used to classify, and it can do so in a generalized manner allowing successful classification of new inputs not used during training. With the explosion of research in emotion in recent year, the application...
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