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Ugunduzi wa Anomaly kwa Kutumia Autoencoder za Nusu-Msimamizi

Ugunduzi wa Anomaly kwa Kutumia Autoencoder za Nusu-Msimamizi hufunza autoencoder ya neural hasa kwa data ya kawaida (isiyo na lebo), kisha hutumia seti ndogo ya anomalies zenye lebo kurekebisha mipaka ya uamuzi, kugundua anomalies kama sampuli zenye hitilafu kubwa ya ujenzi. Inajaza pengo kati ya autoencoders zisizo na msimamizi na vighairi vilivyo na msimamizi kamili wakati lebo ni chache lakini anomalies zinazojulikana zipo.

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Ugunduzi wa Anomaly kwa Kutumia Autoencoder za Nusu-Msimamizi
Uchambuzi wa kiotomatiki…Isolation ForestOne-Class SVMUjifunzaji Nusu-SimamiwaSemi-supervised One-clas…Uchunguzi wa Hitilafu kw…Uchambuzi wa Anomali kwa…Utambuzi wa Anomali kwa…Uchanganuzi wa Anomali k…Ugunduzi wa Anomali kwa…

Vyanzo

  1. Ruff, L., Vandermeulen, R. A., Franks, B. J., Müller, K.-R., & Kloft, M. (2020). Deep Semi-Supervised Anomaly Detection. In International Conference on Learning Representations (ICLR 2020). link ↗
  2. Zong, B., Song, Q., Min, M. R., Cheng, W., Lumezanu, C., Cho, D., & Chen, H. (2018). Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection. In International Conference on Learning Representations (ICLR 2018). link ↗

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Semi-supervised Autoencoder-based Anomaly Detection. ScholarGate. https://scholargate.app/sw/machine-learning/semi-supervised-autoencoder-anomaly-detection

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Imerejelewa na

Uchunguzi wa Hitilafu kwa Kutumia Kujifunza Amilifu na Kirejeshi KiotomatikiUchambuzi wa Anomali kwa Kutumia Bayesian AutoencoderUtambuzi wa Anomali kwa Kutumia Ensemble AutoencoderUchanganuzi wa Anomali kwa Kutumia Autoencoder MtandaoniUgunduzi wa Anomali kwa Kutumia Autoencoder wa Kujifundisha Pekee

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ScholarGate — Semi-supervised Autoencoder Anomaly Detection (Semi-supervised Autoencoder-based Anomaly Detection). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/machine-learning/semi-supervised-autoencoder-anomaly-detection · Seti ya data: https://doi.org/10.5281/zenodo.20539026