方法证据记录
Self-supervised Decision Tree
Self-supervised Decision Tree learning combines the interpretability of classical decision trees with the ability to exploit large quantities of unlabeled data through self-supervised pretext tasks. The model learns useful feature representations or node-split criteria from unlabeled samples before refining predictions on a small labeled set, bridging the gap between fully supervised trees and purely unsupervised clustering.
源记录
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Self-supervised Decision Tree Learning
分类方法记录 · ml-model / machine-learning
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