Machine learningMachine learning
自监督朴素贝叶斯
自监督朴素贝叶斯（Self-supervised Naive Bayes）通过期望最大化（Expectation-Maximization, EM）迭代循环，利用大量未标记数据分配软伪标签，从而扩展了经典的朴素贝叶斯分类器。该方法最初由 Nigam 等人（2000）在文本分类任务上进行了论证，当标记样本稀缺但未标记数据充足时，能够显著提高准确率。
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来源
- 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
如何引用本页
ScholarGate. (2026, June 3). Self-supervised Naive Bayes (EM-augmented Generative Classifier). ScholarGate. https://scholargate.app/zh/machine-learning/self-supervised-naive-bayes
Which method?
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.
- 朴素贝叶斯 (Naive Bayes) 是一种快速的概率分类器，它应用贝叶斯定理，同时假设特征在给定类别时是条件独立的机器学习↔ compare
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