Depth based classification method on the basis of a scalable Mahalanobis distance for sets with unequal prior probabilities

Збережено в:
Бібліографічні деталі
Дата:2016
Автор: A. A. Galkin
Формат: Стаття
Мова:Англійська
Опубліковано: 2016
Назва видання:International Scientific Technical Journal «Problems of Control and Informatics»
Онлайн доступ:http://jnas.nbuv.gov.ua/article/UJRN-0001292607
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Назва журналу:Library portal of National Academy of Sciences of Ukraine | LibNAS

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Library portal of National Academy of Sciences of Ukraine | LibNAS
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spelling open-sciencenbuvgovua-570772024-02-29T13:27:07Z Depth based classification method on the basis of a scalable Mahalanobis distance for sets with unequal prior probabilities A. A. Galkin 2786-6491 2016 en International Scientific Technical Journal «Problems of Control and Informatics» http://jnas.nbuv.gov.ua/article/UJRN-0001292607 Article
spellingShingle International Scientific Technical Journal «Problems of Control and Informatics»
A. A. Galkin
Depth based classification method on the basis of a scalable Mahalanobis distance for sets with unequal prior probabilities
title Depth based classification method on the basis of a scalable Mahalanobis distance for sets with unequal prior probabilities
title_full Depth based classification method on the basis of a scalable Mahalanobis distance for sets with unequal prior probabilities
title_fullStr Depth based classification method on the basis of a scalable Mahalanobis distance for sets with unequal prior probabilities
title_full_unstemmed Depth based classification method on the basis of a scalable Mahalanobis distance for sets with unequal prior probabilities
title_short Depth based classification method on the basis of a scalable Mahalanobis distance for sets with unequal prior probabilities
title_sort depth based classification method on the basis of a scalable mahalanobis distance for sets with unequal prior probabilities
url http://jnas.nbuv.gov.ua/article/UJRN-0001292607
work_keys_str_mv AT aagalkin depthbasedclassificationmethodonthebasisofascalablemahalanobisdistanceforsetswithunequalpriorprobabilities