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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
Publikováno v:
ACM/IMS Journal of Data Science; Sep2024, Vol. 1 Issue 3, p1-42, 42p
Publikováno v:
Working Papers (Faculty) - Stanford Graduate School of Business. Feb2023, p1-23. 23p.
Publikováno v:
PLoS ONE. 6/1/2017, Vol. 12 Issue 6, p1-18. 18p.
Autor:
Riekeles, Max1 (AUTHOR) j.schirmack@tu-berlin.de, Schirmack, Janosch1 (AUTHOR) dirksm@wsu.edu, Schulze-Makuch, Dirk1,2,3,4 (AUTHOR)
Publikováno v:
Life (2075-1729). Jan2021, Vol. 11 Issue 1, p44. 1p.