Zobrazeno 1 - 10
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pro vyhledávání: '"Non-Linear Support Vector Machines"'
Akademický článek
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Autor:
Aunsa Shah, Muhammad Ahmer, Bhawani Shankar Chowdhry, M Z Abbas Shah, Syed Sakhawat Shah, Syed Mujtaba Shah, Khadim Hussain Bhatti
Publikováno v:
Indian Journal of Science and Technology. 10:1-8
Activities of Daily Living (ADL) refers to different daily routine type activities which includes walking, running, jogging, standing, sitting etc. Recognition of ADLs has been of considerable interest to researchers for health assessment purposes. F
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4905c1422dcd7b3aaa22f2a5ef2a95b9
https://doi.org/10.17485/ijst/2017/v10i36/119182
https://doi.org/10.17485/ijst/2017/v10i36/119182
Publikováno v:
Advances in Intelligent Networking and Collaborative Systems ISBN: 9783319985565
INCoS
INCoS
Support vector machine (SVM) is a popular classifier that has been used to solve a broad range of problems. Unfortunately, its applications are limited by computational complexity of training which is \(O(t^3)\), where t is the number of vectors in t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::753b4bb04fe3ba3d8fffa795e5d604c9
https://doi.org/10.1007/978-3-319-98557-2_47
https://doi.org/10.1007/978-3-319-98557-2_47
Kniha
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Autor:
Ru Zhang
Publikováno v:
Applied Mechanics and Materials. :1201-1204
With the ceramics market's developing, the use of image processing and intelligent algorithm is applied to the ancient ceramics recognition and appreciation is one of the most challenging issues in the field of ancient ceramics. Article focuses on se
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::23e45afddcbcc544bc1407c99d56c935
https://doi.org/10.4028/www.scientific.net/amm.278-280.1201
https://doi.org/10.4028/www.scientific.net/amm.278-280.1201
Publikováno v:
ICCTA
Support vector machines (SVMs) are hyperplane classifiers defined in a kernel induced feature space. The data size dependent training time complexity of SVMs usually prohibits its use in applications involving more than a few thousands of data points
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::19ac83d1250f66b1d5a35706c55b9f12
https://doi.org/10.1109/iccta.2007.39
https://doi.org/10.1109/iccta.2007.39
Publikováno v:
IndraStra Global.
Support Vector Machines(SVMs) are hyperplane classifiers defined in a kernel induced feature space. The data size dependent training time complexity of SVMs usually prohibits its use in applications involving more than a few thousands of data points.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=issn23813652::c7ed441f9c945b3edb540f02b3c0a749
https://igi.indrastra.com/items/show/187863
https://igi.indrastra.com/items/show/187863
Autor:
Chapelle, O., Schölkopf, B.
Publikováno v:
Advances in Neural Information Processing Systems 14
The choice of an SVM kernel corresponds to the choice of a representation of the data in a feature space and, to improve performance, it should therefore incorporate prior knowledge such as known transformation invariances. We propose a technique whi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1874::94f2896793a7deded64c151bb2f52b05
https://hdl.handle.net/11858/00-001M-0000-0013-DF0B-7
https://hdl.handle.net/11858/00-001M-0000-0013-DF0B-7
Autor:
Zhang, Ru
Publikováno v:
Applied Mechanics and Materials; January 2013, Vol. 278 Issue: 1 p1201-1204, 4p
Conference
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