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网络的推理和验证

Inference and Validation of Networks
课程网址: http://videolectures.net/ecmlpkdd09_flaounas_ivn/  
主讲教师: Ilias Flaounas
开课单位: 布里斯托尔大学
开课时间: 2009-10-20
课程语种: 英语
中文简介:
我们基于统计测试和机器学习的原理,开发了一种统计方法来验证网络推理算法的结果。通过相似性度量和空模型将结果与参考网络进行比较,使我们能够测量结果的显着性及其预测能力。广义线性模型的使用允许我们根据我们期望部分相关的可用基础事实来解释结果。我们提出了这些方法,用于根据他们对故事的偏好来推断新闻网络的网络。我们比较了三种简单的网络推理方法,并展示了如何使用我们的技术在它们之间进行选择。此处介绍的所有方法都可以直接应用于使用网络推理的其他域。
课程简介: We develop a statistical methodology to validate the result of network inference algorithms, based on principles of statistical testing and machine learning. The comparison of results with reference networks, by means of similarity measures and null models, allows us to measure the significance of results, as well as their predictive power. The use of Generalised Linear Models allows us to explain the results in terms of available ground truth which we expect to be partially relevant. We present these methods for the case of inferring a network of News Outlets based on their preference of stories to cover. We compare three simple network inference methods and show how our technique can be used to choose between them. All the methods presented here can be directly applied to other domains where network inference is used.
关 键 词: 机器学习; 统计方法; 相似性度量
课程来源: 视频讲座网
最后编审: 2019-03-24:cwx
阅读次数: 86