Machine learningMachine learning

Jednoklasni SVM

Jednoklasni SVM (One-class SVM) je algoritam za detekciju anomalija i novina bez nadzora, koji u prostoru obeležja indukovanom jezgrom uči tesnu granicu oko normalnih podataka za obuku, označavajući nova zapažanja koja padaju izvan te granice kao autlajere. Uveli su ga Scholkopf et al. 1999–2001. godine, a proširuje SVM okvir na postavku sa jednom klasom gde nisu dostupne obeležene anomalije.

Otvorite u MethodMindUskoroVideoUskoroDownload slides

Pročitajte celu metodu

Samo za članove

Prijavite se besplatnim nalogom da biste pročitali ovaj odeljak.

Prijavite se

Method map

The neighbourhood of related methods — select a node to explore.

+18 more

Izvori

  1. 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
  2. Tax, D. M. J., & Duin, R. P. W. (2004). Support vector data description. Machine Learning, 54(1), 45–66. DOI: 10.1023/B:MACH.0000008084.60811.49

Kako citirati ovu stranicu

ScholarGate. (2026, June 3). One-Class Support Vector Machine (Novelty and Anomaly Detection). ScholarGate. https://scholargate.app/sr/machine-learning/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.

Compare side by side

Citirana u

ScholarGateOne-class SVM (One-Class Support Vector Machine (Novelty and Anomaly Detection)). Preuzeto 2026-06-15 sa https://scholargate.app/sr/machine-learning/one-class-svm · Skup podataka: https://doi.org/10.5281/zenodo.20539026