概率图分区][Probabilistic graph partitioning]

概率图分区 Probabilistic graph partitioning


课程网址:	http://videolectures.net/sicgt07_barber_pgp/
主讲教师:	David Barber
开课单位:	伦敦大学学院
开课时间:	2007-09-07
课程语种:	英语
中文简介:	我们考虑了Web挖掘和协同过滤中的应用程序的图形分区问题。我们的方法基于基于概率混合模型的形式预测有向链路的存在/不存在。基于有向图的生成模型，我们能够应用近似贝叶斯处理来自动选择适当数量的分区。我们将讨论协同过滤中的应用，并评论混合成员模型，潜在Dirichlet分配和概率潜在语义分析之间的关系。
课程简介:	We consider the problem of Graph Partitioning for applications in Web Mining and Collaborative Filtering. Our approach is based on predicting the presence/absence of a directed link based on a form of probabilistic mixture model. Being based on a generative model of directed graphs, we are able to apply an approximate Bayesian treatment to automatically select an appropriate number of partitions. We will discuss an application in Collaborative Filtering and comment on relations to mixed membership models, Latent Dirichlet Allocation and Probabilistic Latent Semantic Analysis.
关键词:	Web挖掘; 图形分区; 概率混合模型
课程来源:	视频讲座网
最后编审:	2019-09-17：lxf
阅读次数:	73

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