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Mixed sum-product and convolutional networks for classification problems
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| dc.contributor.author | BADEA, Maria-Alexandra | |
| dc.contributor.author | PELICAN, Elena | |
| dc.contributor.author | VERNIC, Raluca | |
| dc.date.accessioned | 2024-12-08T10:24:44Z | |
| dc.date.available | 2024-12-08T10:24:44Z | |
| dc.date.issued | 2024 | |
| dc.identifier.citation | BADEA, Maria-Alexandra; Elena PELICAN and Raluca VERNIC. Mixed sum-product and convolutional networks for classification problems. In: Electronics, Communications and Computing (IC ECCO-2024): The conference program and abstract book: 13th intern. conf., Chişinău, 17-18 Oct. 2024. Technical University of Moldova. Chişinău: Tehnica-UTM, 2024, p. 175. ISBN 978-9975-64-480-8 (PDF). | en_US |
| dc.identifier.isbn | 978-9975-64-480-8 | |
| dc.identifier.uri | http://repository.utm.md/handle/5014/28803 | |
| dc.description | Only Abstract | en_US |
| dc.description.abstract | In this work, we propose a joint Sum-Product (SPN) and Convolutional (CNN) Network for classification problems, namely image classification performed on several real-life benchmark datasets. These mixed networks represent an original development within the probabilistic graphical models domain that outperform the traditional CNNs results. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Technical University of Moldova | en_US |
| dc.relation.ispartofseries | Electronics, Communications and Computing (IC ECCO-2024): 13th intern. conf., 17-18 Oct. 2024; | |
| dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
| dc.subject | sum-product networks | en_US |
| dc.subject | convolutional networks | en_US |
| dc.subject | classification problems | en_US |
| dc.subject | probabilistic inference | en_US |
| dc.title | Mixed sum-product and convolutional networks for classification problems | en_US |
| dc.type | Article | en_US |
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2024
The 13th International Conference on Electronics, Communications and Computing (IC ECCO-2024)
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