方法证据记录
Semi-supervised Naive Bayes
Semi-supervised Naive Bayes extends the classic Naive Bayes generative model to exploit large pools of unlabeled data alongside a small labeled set. Using Expectation-Maximization, it iteratively infers soft class assignments for unlabeled examples and re-estimates class and feature parameters, yielding substantially better classifiers when labeled examples are scarce.
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 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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