L. Skovajsova

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The paper describes the neural network model which in the information retrieval process solves the document set dimension reduction for representation of text documents in Slovak language. This model comes out of the vector space model, which for document set uses the full index representation. To decrease the matrix dimension for document set(More)
This paper presents the comparison of the text document space dimension reduction and the text document clustering and also the keyword space dimension reduction and keyword clustering by the latent semantic analysis and by the Hebbian neural network with Oja learning rule. Results of this neural network are compared with the results of the latent semantic(More)
The paper deals with utilization of neural networks for information retrieval. It is focused on reduction of text document space by Hebbian neural networks. The Hebbian neural network with Oja learning rule with linear activation function reduces term space into much lower dimension and gives good results for text document dimension reduction and retrieval.(More)
This paper presents text document space dimension reduction in text document retrieval by two different neural networks and their comparison. First neural network is Hebbian-type neural network, and second neural network is autoassociative neural network which uses backpropagation learning rule. Both neural networks reduce document space to two dimensions(More)
This paper shows text document dimension reduction and clustering technique which is called the bigradient learning algorithm. This algorithm is based on the two learning parameters. The results show, that bigradient learning algorithm, used with proper selected values, does almost the same clustering as the other arbitrary PCA learning method by neural(More)
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