Ensemble One-Class SVM
Ensemble One-Class SVM kombinuje više modela jedne klase SVM — svaki obučen na različitom slučajnom podskupu podataka ili karakteristika — i agregira njihove rezultate anomalija. Kombinovanjem nekoliko procena granica OC-SVM, ansambl smanjuje osetljivost na izbor kernela i uzorkovanje podataka koje pogađa pojedinačni one-class SVM, proizvodeći stabilniji i precizniji detektor novina ili odstupanja.
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Izvori
- Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471. DOI: 10.1162/089976601750264965 ↗
- Tax, D. M. J., & Duin, R. P. W. (2001). Combining one-class classifiers. In Multiple Classifier Systems (MCS 2001), Lecture Notes in Computer Science, vol 2096. Springer, Berlin, Heidelberg. DOI: 10.1007/3-540-48219-9_30 ↗
Kako citirati ovu stranicu
ScholarGate. (2026, June 3). Ensemble of One-Class Support Vector Machines. ScholarGate. https://scholargate.app/sr/machine-learning/ensemble-one-class-svm
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Autoenkoderska detekcija anomalijaMašinsko učenje↔ compare
- Isolation ForestMašinsko učenje↔ compare
- Jednoklasni SVMMašinsko učenje↔ compare
- Glasački ansamblMašinsko učenje↔ compare
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