方法证据记录
Self-supervised Isolation Forest
Self-supervised Isolation Forest augments the classic Isolation Forest anomaly detector with a self-supervised pre-training stage. A pretext task — such as predicting rotation, masked features, or contrastive pairs — is solved without labels to learn a richer feature representation, which is then used when building the isolation trees, yielding sharper anomaly scores on complex, high-dimensional tabular data.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Self-supervised Isolation Forest (SSL-augmented Anomaly Detection)
分类方法记录 · ml-model / machine-learning
- Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM), pp. 413–422. · DOI 10.1109/ICDM.2008.17
- Isolation Forest. Wikipedia. · URL
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