Vol. 2 No. 6 (2025): Conference on Social Sciences and Humanities Research (CSSHR)
Articles

BIR SINFLI TAYANCH VEKTOR MASHINALARI (ONE-CLASS SVM) ASOSIDA SHAXS IMZOSINI ANIQLASHNING SAMARADORLIGI

Umidjon Axundjanov
Farg‘ona davlat texnika universiteti Texnika fanlari nomzodi, dotsent
Marina Lazareva
Farg‘ona davlat texnika universiteti dotsenti
Xamidullo Ashuraliyev
Farg‘ona davlat texnika universiteti Kompyuter tizimlari va ularning dasturiy ta’minoti yo’nalishi magistranti

Published 2025-06-12

Abstract

Mazkur maqolada shaxs imzosini avtomatik tarzda aniqlash va verifikatsiyalashda bir sinfli tayanch vektor mashinalari (One-Class SVM) modelidan foydalanish imkoniyatlari ko‘rib chiqiladi. Modelning ishlash prinsipi, uning boshqa usullardan afzalliklari va real eksperimental natijalar asosida samaradorlik darajasi yoritiladi.

References

  1. Schölkopf, 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.
  2. Tax, D.M.J. & Duin, R.P.W. (2004). Support vector data description. Machine Learning, 54(1), 45–66.
  3. Hafemann, L.G., Oliveira, L.S., & Sabourin, R. (2017). Offline handwritten signature verification — literature review. arXiv preprint arXiv:1705.05787.
  4. Riesz, C., Ferrer, M.A., & Impedovo, D. (2019). Signature verification: State-of-the-art and current challenges. Computer Vision and Image Understanding, 181, 1–20.
  5. Chollet, F. (2018). Deep Learning with Python. Manning Publications.
  6. Vapnik, V. (1998). Statistical Learning Theory. Wiley-Interscience.
  7. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
  8. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.