Machine learningMachine learning
集成单类支持向量机 (Ensemble One-Class SVM)
集成单类支持向量机 (Ensemble One-Class SVM) 结合了多个单类支持向量机 (one-class support vector machine, OC-SVM) 模型——每个模型都在数据的不同随机子集或特征上进行训练——并聚合它们的异常分数。通过汇集多个 OC-SVM 的边界估计，该集成方法降低了单一 OC-SVM 所受到的核函数选择和数据采样敏感性影响，从而产生更稳定、更准确的新颖性或异常值检测器。
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来源
- 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 ↗
- Tax, D. M. J., & Duin, R. P. W. (2001). Combining one-class classifiers. In Multiple Classifier Systems (MCS 2001), Lecture Notes in Computer Science, vol 2096. Springer, Berlin, Heidelberg. DOI: 10.1007/3-540-48219-9_30 ↗
如何引用本页
ScholarGate. (2026, June 3). Ensemble of One-Class Support Vector Machines. ScholarGate. https://scholargate.app/zh/machine-learning/ensemble-one-class-svm
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Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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