Iyini i-SVM?
Context:Ekufundeni komshini, imishini ye-support vector (ama-SVM, futhi asekela amanethiwekhi we-vector) ayizimodeli zokufunda eziqondisiwe ezinama-algorithms wokufunda ahlobene ahlola idatha yokuhlukaniswa nokuhlaziywa kokuhlehla. Ethuthukiswe e-AT & T Bell Laboratories nguVladimir Vapnik kanye nozakwabo (Boser et al., 1992, Guyon et al., 1993, Cortes and Vapnik, 1995, Vapnik et al., 1997 [citation needed]) ama-SVM angenye yezindlela eziqinile kakhulu zokubikezela, ngokusekelwe ezinhlakeni zokufunda izibalo noma i-VC theory ehlongozwa yi-Vapnik (1982, 1995) neChervonenkis (1974). Njengoba kunikezwe isethi yezibonelo zokuqeqeshwa, ngayinye imakwe njengengenye yezigaba ezimbili, i-algorithm yokuqeqeshwa kwe-SVM yakha imodeli enikeza izibonelo ezintsha kusigaba esisodwa noma kwesinye, okwenza kube yi-non-probabilistic binary linear classifier (yize izindlela ezinjengePlatt scaling zikhona ukusebenzisa i-SVM kusethingi yokuhlukaniswa okungenzeka). I-SVM ihlela izibonelo zokuqeqeshwa zibe amaphuzu esikhaleni ukuze ikhulise ububanzi begebe phakathi kwalezi zigaba ezimbili. Khona-ke izibonelo ezintsha zibekwa kuleyo ndawo efanayo futhi kubikezelwa ukuthi ziyingxenye yesigaba esisekelwe ohlangothini lwesikhala eziwela kuso.  Ngaphezu kokwenza ukuhlukaniswa komugqa, ama-SVM angenza ngempumelelo ukuhlukaniswa okungekhona komugqa ngokusebenzisa lokho okubizwa ngokuthi i-kernel trick, ngokusobala abeka okokufaka kwabo esikhaleni sezici ezinobukhulu obuphakeme.  I- support vector clustering algorithm, eyenziwe nguHava Siegelmann noVladimir Vapnik, isebenzisa izibalo ze- support vectors, ezakhiwe ku- support vector machines algorithm, ukuhlukanisa idatha engabhalisiwe. Lezi zinqwaba zedatha zidinga izindlela zokufunda ezingaqondisiwe, ezizama ukuthola ukuqoqwa kwemvelo kwedatha kumaqembu, bese, ukufaka idatha entsha ngokuya ngalezi zinqwaba.
I-SVM imele "umshini wokusekela i-vector". Kuyimodeli yokufunda ngomshini yemisebenzi yokufunda eqondisiwe. Yayidumile ngawo-1990 nasekuqaleni kwawo-2000, ngaphambi kokuba kuqale ukufunda okujulile. Icala lokusebenzisa elivamile ukuhlukaniswa kanambambili, kodwa ukunwetshwa kwalo kungenza futhi ukuhlukaniswa kwamalebula amaningi njengoba senza ukuhlehla. Izindlela ze-Kernel zithuthukise kakhulu ikhono le-SVM lokwenza imisebenzi yokufunda komshini engeyona eyomugqa.