MULTILAYER ADAPTIVE FUZZY PROBABILISTIC NEURAL NETWORK IN CLASSIFICATION PROBLEMS OF TEXT DOCUMENTS
Науковий журнал «Радіоелектроніка, інформатика, управління»
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MULTILAYER ADAPTIVE FUZZY PROBABILISTIC NEURAL NETWORK IN CLASSIFICATION PROBLEMS OF TEXT DOCUMENTS |
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Bodyanskiy, Ye. V.; Kharkiv National University of Radioelectronics, Kharkiv, Ukraine Ryabova, N. V.; Kharkiv National University of Radioelectronics, Kharkiv, Ukraine Zolotukhin, O. V.; Kharkiv National University of Radioelectronics, Kharkiv, Ukraine |
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classification, adaptive fuzzy probabilistic neural network, overlapping classes, neurons in the data points. |
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The problem of text documents classification based on fuzzy probabilistic neural network in real time mode is considered. A different<br />number of classes, which may include such documents, can be allocated in an array of text documents. It is assumed that the data classes can<br />have an n-dimensional space of different shape and mutually overlap. The architecture of the multlayer adaptive fuzzy probabilistic neural<br />network, which allow to solve the problem of classification in sequential mode as new data become available, is.proposed. An algorithm for<br />training the multilayer adaptive fuzzy probabilistic neural network is proposed, and the problem of classification is solved on the basis of the<br />proposed architecture in terms of intersecting classes, which allows to determine the belonging a single instance of a text document to different<br />classes with varying degrees of probability. Classifying neural network architecture characterized by simple numerical implementation and high<br />speed training, and is designed to handle large data sets, characterized by the feature vectors of high dimension. The proposed neural network<br />and its learning method designed to work in conditions of overlapping classes, differing both the form and size. |
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Zaporizhzhya National Technical University 2015-06-23 10:32:00 |
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application/pdf http://ric.zntu.edu.ua/article/view/45115 |
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Radio Electronics, Computer Science, Control; No 1 (2015): Radio Electronics, Computer Science, Control |
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ru |
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Copyright (c) 2015 Ye. V. Bodyanskiy, N. V. Ryabova, O. V. Zolotukhin |
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