Machine learningMachine learning
主动学习高斯混合模型
主动学习高斯混合模型（Active Learning Gaussian Mixture Model）结合了迭代式查询策略和高斯混合模型学习器。该算法选择信息量最大的未标记点——通常是预测不确定性最高者——将其呈现给一个预言机（oracle）进行标记，然后使用EM算法在不断增长的标记数据集上重新拟合GMM。其结果是一个密度模型，能够达到全数据质量，同时所需的标记样本数量大大减少。
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来源
- Zhu, X., Ghahramani, Z., & Lafferty, J. (2003). Semi-supervised learning using Gaussian fields and harmonic functions. Proceedings of the 20th International Conference on Machine Learning (ICML), 912–919. link ↗
- Settles, B. (2012). Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 6(1), 1–114. Morgan & Claypool Publishers. DOI: 10.2200/S00429ED1V01Y201207AIM018 ↗
如何引用本页
ScholarGate. (2026, June 3). Active Learning with Gaussian Mixture Model. ScholarGate. https://scholargate.app/zh/machine-learning/active-learning-gaussian-mixture-model
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