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pro vyhledávání: '"Julian Zimmert"'
Publikováno v:
PLoS ONE, Vol 12, Iss 6, p e0178161 (2017)
Training of one-vs.-rest SVMs can be parallelized over the number of classes in a straight forward way. Given enough computational resources, one-vs.-rest SVMs can thus be trained on data involving a large number of classes. The same cannot be stated
Externí odkaz:
https://doaj.org/article/061471f88a47404785ce126bd0a1fd7d
Publikováno v:
PLoS ONE
PLoS ONE, Vol 12, Iss 6, p e0178161 (2017)
PLoS ONE, Vol 12, Iss 6, p e0178161 (2017)
Training of one-vs.-rest SVMs can be parallelized over the number of classes in a straight forward way. Given enough computational resources, one-vs.-rest SVMs can thus be trained on data involving a large number of classes. The same cannot be stated
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::baf810eb2bcf973ecc422717ecaa908f
http://europepmc.org/articles/PMC5453486
http://europepmc.org/articles/PMC5453486