Articolul precedent |
Articolul urmator |
222 3 |
Ultima descărcare din IBN: 2023-06-14 07:34 |
SM ISO690:2012 PENTIUC, Stefan-Gheorghe, BOBRIC, Elena Crenguța, BILIUS, Laura-Bianca. Analysis with Unsupervised Learning Based Techniques of Load Factor Profiles and Hyperspectral Images. In: Electronics, Communications and Computing, Ed. 12, 20-21 octombrie 2022, Chişinău. Chișinău: Tehnica-UTM, 2023, Editia 12, pp. 136-139. DOI: https://doi.org/10.52326/ic-ecco.2022/SEC.05 |
EXPORT metadate: Google Scholar Crossref CERIF DataCite Dublin Core |
Electronics, Communications and Computing Editia 12, 2023 |
||||||
Conferința "Electronics, Communications and Computing" 12, Chişinău, Moldova, 20-21 octombrie 2022 | ||||||
|
||||||
DOI:https://doi.org/10.52326/ic-ecco.2022/SEC.05 | ||||||
Pag. 136-139 | ||||||
|
||||||
Descarcă PDF | ||||||
Rezumat | ||||||
The problem of obtaining an optimal partition consistent with a series of partitions resulting from the application of various clustering algorithms is NP complete. A heuristic method based on the concepts of central partition and strong patterns developed by Edwin Diday [3] is proposed. It is presented the experience regarding the use of analysis techniques based on unsupervised learning methods of load factor profiles and hyperspectral images. |
||||||
Cuvinte-cheie machine learning, unsupervised learning, Clustering algorithms, load factor profiles, hyperspectral images |
||||||
|