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Kohonen's self-organizing map (SOM) is a competitive learning neural network that uses a neighborhood lateral interaction function to discover the topological structure hidden in the data set. In general, the SOM neural network is constructed as a learning algorithm for numerical data. However, except these numeric data, there are many other data types such as symbolic data. Thus, Yang et al. proposed...
The task of presenting an optimal personalized learning path in an educational hypermedia system requires much effort and cost particularly in defining rules for the adaptation of learning materials. This research focuses on the adaptive course sequencing method that uses soft computing techniques as an alternative to a rule-based adaptation for an adaptive learning system. In this paper we present...
The classification for similar features classes is quite difficult task in many existing pattern-recognition systems. When the amount of samples is insufficient, neural networking training is hard. The dimension reduction, classification, clustering etc serial steps in recognition process takes such much time that the practical recognizing application is ease to meet the real time requirement. The...
Many of the widely used classifiers are time consuming and resource intensive, and hence not practical to be used in the emerging wireless networks. We present an efficient classifier, termed distributed hierarchical graph neuron (DHGN)-based classifier. Our proposed solution uses a new form of neural network, which consists of a hierarchical graph-based representation of input patterns, and adopts...
This paper proposes the new approach to deal with the classification problems by modifying the well-known Kohonen self- organizing map in order to make it able to solve classification problems. During training, the fuzzy membership function is used in place of the Euclidean distance to find the best matching cluster for the input pattern. In order to improve the efficiency of proposed model, the fuzzy...
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