Boosted learning in dynamic Bayesian networks for multimodal detection
Boosted learning in dynamic Bayesian networks for multimodal detection
复制标题
增强动态贝叶斯网络的学习以实现多模态检测
DOI：
10.1109/icif.2002.1021202
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发表时间：
2002
期刊：
影响因子：
--
通讯作者：
A. Pentland
中科院分区：
文献类型：
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作者：
Tanzeem Chaodhury;James M. Rehg;V. Pavlovic;A. Pentland
贝叶斯网络是人类感知的一种有吸引力的建模工具，因为它们将直观的图形表示与有效的推理和学习算法相结合。多个传感器的时间融合可以有效地制定使用动态贝叶斯网络（DBN），它允许统计推断和学习的力量与问题的上下文知识相结合。不幸的是，简单的学习方法可能会导致这种吸引人的模型失败时，数据表现出复杂的行为，我们首先展示了如何提升参数学习可以用来提高复杂的多模态推理问题的贝叶斯网络分类器的性能。作为一个例子，我们应用框架的问题，视听扬声器检测在交互式环境中使用“现成的”视觉和音频传感器（脸，皮肤，纹理，嘴运动，和沉默检测器）。然后，我们介绍了一个提升的结构学习算法。在给定标记数据的情况下，我们的算法修改了网络结构和参数，从而提高了分类精度。我们将其性能与标准结构学习和增强参数学习进行了比较。我们目前的结果，从UCI存储库的说话人检测和数据集。
Bayesian networks are an attractive modeling tool for human sensing, as they combine an intuitive graphical representation with efficient algorithms for inference and learning. Temporal fusion of multiple sensors can be efficiently formulated using dynamic Bayesian networks (DBNs) which allow the power of statistical inference and learning to be combined with contextual knowledge of the problem. Unfortunately, simple learning methods can cause such appealing models to fail when the data exhibits complex behavior We first demonstrate how boosted parameter learning could be used to improve the performance of Bayesian network classifiers for complex multimodal inference problems. As an example we apply the framework to the problem of audiovisual speaker detection in an interactive environment using "off-the-shelf" visual and audio sensors (face, skin, texture, mouth motion, and silence detectors). We then introduce a boosted structure learning algorithm. Given labeled data, our algorithm modifies both the network structure and parameters so as to improve classification accuracy. We compare its performance to both standard structure learning and boosted parameter learning. We present results for speaker detection and for datasets from the UCI repository.