Machine learningMachine learning

Jednoklasni SVM

Jednoklasni SVM (One-class SVM) je nenadzorovani algoritam za detekciju anomalija i noviteta koji u prostoru značajki induciranom kernelom uči usku granicu oko normalnih podataka za treniranje, označavajući nove promatranja koja padaju izvan te granice kao odstupanja. Predstavljen od strane Scholkopfa i suradnika 1999.–2001., proširuje SVM okvir na postavku jedne klase gdje nisu dostupne označene anomalije.

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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/hr/machine-learning/one-class-svm

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