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半监督单类支持向量机

半监督单类支持向量机（Semi-supervised One-class SVM）通过结合少量已知的正常样本和大量未标记的观测数据，扩展了经典的单类支持向量机异常检测器。未标记数据有助于模型在特征空间中学习到更紧凑、信息量更丰富的决策边界，从而减少假阳性并提高异常召回率，优于纯粹的无监督基线方法。

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半监督单类支持向量机
自动编码器异常检测高斯过程孤立森林 (Isolation Forest)单类支持向量机半监督学习自监督单类支持向量机 (Self-supervi…半监督自编码器异常检测

来源

  1. Munoz, A. & Muruzabal, J. (2004). Self-Organising Maps for Outlier Detection. Neurocomputing, 58–60, 953–956. link ↗
  2. 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 ↗

如何引用本页

ScholarGate. (2026, June 3). Semi-supervised One-Class Support Vector Machine. ScholarGate. https://scholargate.app/zh/machine-learning/semi-supervised-one-class-svm

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被引用于

自监督单类支持向量机 (Self-supervised One-class SVM)半监督自编码器异常检测

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ScholarGate — Semi-supervised One-class SVM (Semi-supervised One-Class Support Vector Machine). 于 2026-06-15 检索自 https://scholargate.app/zh/machine-learning/semi-supervised-one-class-svm · 数据集： https://doi.org/10.5281/zenodo.20539026