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首页> 外文期刊>IEEE Transactions on Pattern Analysis and Machine Intelligence >Variational Bayes for continuous hidden Markov models and its application to active learning
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Variational Bayes for continuous hidden Markov models and its application to active learning

机译：连续隐马尔可夫模型的变分贝叶斯及其在主动学习中的应用

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摘要

In this paper, we present a variational Bayes (VB) framework for learning continuous hidden Markov models (CHMMs), and we examine the VB framework within active learning. Unlike a maximum likelihood or maximum a posteriori training procedure, which yield a point estimate of the CHMM parameters, VB-based training yields an estimate of the full posterior of the model parameters. This is particularly important for small training sets since it gives a measure of confidence in the accuracy of the learned model. This is utilized within the context of active learning, for which we acquire labels for those feature vectors for which knowledge of the associated label would be most informative for reducing model-parameter uncertainty. Three active learning algorithms are considered in this paper: 1) query by committee (QBC), with the goal of selecting data for labeling that minimize the classification variance, 2) a maximum expected information gain method that seeks to label data with the goal of reducing the entropy of the model parameters, and 3) an error-reduction-based procedure that attempts to minimize classification error over the test data. The experimental results are presented for synthetic and measured data. We demonstrate that all of these active learning methods can significantly reduce the amount of required labeling, compared to random selection of samples for labeling.
机译：在本文中，我们提出了一种变分贝叶斯（VB）框架，用于学习连续隐马尔可夫模型（CHMM），并研究了主动学习中的VB框架。与产生CHMM参数的点估计的最大似然或最大后验训练程序不同，基于VB的训练产生模型参数的全部后验的估计。这对于小型训练集尤其重要，因为它可以对学习模型的准确性进行置信度测量。这是在主动学习的上下文中利用的，为此我们获取了那些特征向量的标签，对于这些特征向量，相关标签的知识对于减少模型参数的不确定性最有帮助。本文考虑了三种主动学习算法：1）委员会查询（QBC），目的是选择用于分类的数据，以最大程度地减少分类方差； 2）一种最大预期信息获取方法，其目标是标记数据。减少模型参数的熵，以及3）一种基于减少错误的过程，该过程试图使测试数据的分类错误最小化。给出了用于合成和测量数据的实验结果。我们证明，与随机选择要标记的样本相比，所有这些主动学习方法都可以大大减少所需的标记数量。

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