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Є-insensitive Unsupervised Learning
| dc.contributor.author | Fyfe, C. | ru |
| dc.contributor.author | Gabrys, B. | ru |
| dc.coverage.spatial | Brest | ru |
| dc.date.accessioned | 2021-07-27T08:09:26Z | |
| dc.date.available | 2021-07-27T08:09:26Z | |
| dc.date.issued | 1999 | |
| dc.identifier.citation | Fyfe, C. Є-insensitive Unsupervised Learning / C. Fyfe, B. Gabrys // 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, Ternopil) ; ed. V. Golovko. – Brest : BPI, 1999. – P. 10–18. | ru |
| dc.identifier.uri | https://rep.bstu.by/handle/data/20643 | |
| dc.description.abstract | One of the major paradigms for unsupervised learning in Artificial Neural Networks is Hebbian learning. The standard implementations of Hebbian learning are optimal under the assumptions of Gaussian noise in a data set. We derive e-insensitive Hebbian learning based on minimising the least absolute error in a compressed data set and show that the learning rule is equivalent to the Principal Component Analysis (PCA) networks' learning rules under a variety of conditions. | ru |
| dc.language.iso | en | ru |
| dc.publisher | BPI | ru |
| dc.title | Є-insensitive Unsupervised Learning | ru |
| dc.type | Научный доклад (Working Paper) | ru |
