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Machine learningMachine learning

One-Class SVM

One-class SVM ni algoriti ya ugunduzi wa anomali na ubunifu ambayo haijisimamiwi na hujifunza mpaka mkali unaozunguka data ya mafunzo ya kawaida katika nafasi ya vipengele iliyoanzishwa na kernel, ikionyesha uchunguzi mpya unaoanguka nje ya mpaka huo kama vipengee vya nje. Ilianzishwa na Scholkopf et al. mwaka 1999–2001, inapanua mfumo wa SVM kwa mazingira ya darasa moja ambapo hakuna anomali zilizowekwa lebo zinazopatikana.

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Vyanzo

  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

Jinsi ya kunukuu ukurasa huu

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

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ScholarGateOne-class SVM (One-Class Support Vector Machine (Novelty and Anomaly Detection)). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/machine-learning/one-class-svm · Seti ya data: https://doi.org/10.5281/zenodo.20539026