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https://scidar.kg.ac.rs/handle/123456789/21166
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Поље DC-а | Вредност | Језик |
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dc.contributor.author | Lutovac Banduka, Maja | - |
dc.contributor.author | Poucki, Vladimir | - |
dc.contributor.author | Mladenovic, Vladimir | - |
dc.contributor.author | Lutovac, Miroslav | - |
dc.date.accessioned | 2024-10-08T07:45:02Z | - |
dc.date.available | 2024-10-08T07:45:02Z | - |
dc.date.issued | 2024 | - |
dc.identifier.isbn | 9788677762766 | en_US |
dc.identifier.uri | https://scidar.kg.ac.rs/handle/123456789/21166 | - |
dc.description.abstract | It is presented how the slope of symmetric activation functions with saturation affects class detection using symbolic analysis. Different activation functions can be used to increase the most likely detected classes. The main result is the determination of the highest slope of the activation function and the lowest slope of the activation function in terms of the number of neurons in the layer. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Faculty of Technical Sciences Čačak, University of Kragujevac | en_US |
dc.relation.ispartof | 10th International Scientific Conference Technics, Informatics and Education - TIE 2024 | en_US |
dc.rights | CC0 1.0 Universal | * |
dc.rights.uri | http://creativecommons.org/publicdomain/zero/1.0/ | * |
dc.subject | class detection | en_US |
dc.subject | probability | en_US |
dc.subject | automated drawing | en_US |
dc.subject | symbolic solving of the neural network | en_US |
dc.title | Effect of the Slope of Symmetric Saturated Activation Functions on Deep Learning | en_US |
dc.type | conferenceObject | en_US |
dc.description.version | Published | en_US |
dc.identifier.doi | 10.46793/TIE24.079LB | en_US |
dc.type.version | PublishedVersion | en_US |
Налази се у колекцијама: | Faculty of Technical Sciences, Čačak |
Датотеке у овој ставци:
Датотека | Опис | Величина | Формат | |
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12 - I.10..pdf | 428.6 kB | Adobe PDF | ![]() Погледајте |
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