方法证据记录
Self-supervised Naive Bayes
Self-supervised Naive Bayes extends the classic Naive Bayes classifier to exploit large pools of unlabeled data by iteratively assigning soft pseudo-labels through an Expectation-Maximization loop. Originally demonstrated for text classification by Nigam et al. (2000), the approach can substantially improve accuracy when labeled examples are scarce but unlabeled data are plentiful.
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 Naive Bayes (EM-augmented Generative Classifier)
分类方法记录 · ml-model / machine-learning
- Nigam, K., McCallum, A. K., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39(2-3), 103–134. · DOI 10.1023/A:1007692713085
- Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-supervised Learning. MIT Press. · ISBN 978-0-262-03358-9
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