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Unsupervised Training Algorithm for Recirculation Neural Network
| dc.contributor.author | Golovko, V. | ru |
| dc.contributor.author | Gladyschuk, V. | ru |
| dc.coverage.spatial | Brest | ru |
| dc.date.accessioned | 2021-07-27T08:09:29Z | |
| dc.date.available | 2021-07-27T08:09:29Z | |
| dc.date.issued | 1999 | |
| dc.identifier.citation | Golovko, V. Unsupervised Training Algorithm for Recirculation Neural Network / V. Golovko, V. Gladyschuk // International Conference on Neural Networks and Artificial Intelligence ICNNAI'99 = Международная конференция по нейронным сетям и искусственному интеллекту ICNNAI'99 : Proceedings, Brest, Belarus, 12–15 October 1999 / Brest Polytechnic Institute, Department of Computers and Laboratory of Artificial Neural Networks, Belarus Special Interest Group of International Neural NetWork Society, International Neural NetWork Society, Belarusian State University of Informatics and Radioelectronics (Belarus), Belarusian Academy of Sciences, Institute of Engineering Cybemetics (Belarus), Universidad Politechnica de Valencia (Spain), Institute of Computer Information Technologies (Ukraine, Temrnopil) ; ed. V. Golovko. – Brest : BPI, 1999. – P. 19–25. | ru |
| dc.identifier.uri | https://rep.bstu.by/handle/data/20654 | |
| dc.description.abstract | Unsupemised learning is the great promise of the future. In such training the network is provided with inputs but not with desired outputs. Unsupemised learning is used for the principal component networks. This paper describes a nerw method for training of the recirculation networks. Such method is called a sectioning learning. It is characterized by smali training time and stability of training. | ru |
| dc.language.iso | en | ru |
| dc.publisher | BPI | ru |
| dc.title | Unsupervised Training Algorithm for Recirculation Neural Network | ru |
| dc.type | Научный доклад (Working Paper) | ru |
