方法证据记录
Semi-supervised Active Learning
Semi-supervised Active Learning (SSAL) is a hybrid learning paradigm that combines active learning's selective query strategy with semi-supervised learning's ability to exploit unlabeled data. The model iteratively selects the most informative unlabeled instances for expert annotation while simultaneously leveraging the large pool of unannotated samples to improve its own representations, dramatically reducing labeling costs while maintaining strong predictive accuracy.
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。
Semi-supervised Active Learning (SSAL)
分类方法记录 · ml-model / machine-learning
- Settles, B. (2012). Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool. · DOI 10.2200/S00429ED1V01Y201207AIM018
- Zhu, X. (2005). Semi-supervised learning literature survey. Technical Report 1530, Computer Sciences, University of Wisconsin-Madison. · URL
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