Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Basim Alghabashi"'
Autor:
Simge Akay, Basim Alghabashi, Mohamed Al Mashrgy, Nafiz Arica, Zeinab Arjmandiasl, Muhammad Azam, B. Balasingam, Jamal Bentahar, F. Biondi, Aaron Boda, Nizar Bouguila, Duygu Cakir, Mark Green, Sorin Grigorescu, Baoxin Hu, Xishi Huang, M. Khalghollah, Howard Li, C.J.B. Macnab, Narges Manouchehri, Jun Meng, Afshin Rahimi, P. Ramakrishnan, Jing Ren, Jianguo Wang, Jin Wang
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::79626e32df85d4b5d107eafae2610d8b
https://doi.org/10.1016/b978-0-12-822314-7.00005-5
https://doi.org/10.1016/b978-0-12-822314-7.00005-5
Model-based approaches have been widely utilized to investigate multi-dimensional positive features for the purpose of gaining beneficial knowledge. In fact, feature selection is a critical and challenging task when modeling data that are represented
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0f381366400d8c976a3c6451e306c9b5
https://doi.org/10.1016/b978-0-12-822314-7.00011-0
https://doi.org/10.1016/b978-0-12-822314-7.00011-0
Autor:
Basim Alghabashi, Nizar Bouguila
Publikováno v:
ICMLA
In this paper, an unsupervised learning algorithm of a finite multi-dimensional generalized Gamma mixture model (GGMM) is presented to tackle the issue of simultaneously clustering positive vectors and determining the number of clusters. The paramete
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::095a97c5c0c377ceceacded4ba3c2848
https://doi.org/10.1109/icmla.2018.00183
https://doi.org/10.1109/icmla.2018.00183
Autor:
Basim Alghabashi, Nizar Bouguila
Publikováno v:
iThings/GreenCom/CPSCom/SmartData
Over the last two decades, statistical mixture models have been widely exploited to tackle the issue of data modeling. Examples of statistical mixture models' applications in data modeling include object recognition, speech recognition, information r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ded46fbb829cce4988d2b715a798096b
https://doi.org/10.1109/cybermatics_2018.2018.00158
https://doi.org/10.1109/cybermatics_2018.2018.00158