图 书 馆
数据挖掘图形模型简介Introduction to Graphical Models for Data Mining |
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| 课程网址: | http://videolectures.net/kdd2010_banerjee_igmdm/ |
| 主讲教师: | Arindam Banerjee |
| 开课单位: | 明尼苏达大学 |
| 开课时间: | 2010-10-01 |
| 课程语种: | 英语 |
| 中文简介: |  用于大规模数据挖掘的图形模型构成了统计数据分析中令人振奋的发展，在过去十年中获得了巨大的发展势头。与通常会产生“ i.i.d.”的传统统计模型不同假设，图形模型确认感兴趣变量之间的依赖关系，并在考虑此类依赖关系的同时调查推理/预测。近年来，潜在的变量贝叶斯网络（例如潜在的狄利克雷分配，随机块模型，贝叶斯协同聚类和概率矩阵分解技术）在各种应用领域中取得了空前的成功，包括主题建模和文本挖掘，推荐系统，多关系数据分析等。本教程将对图形模型进行广泛的概述，并讨论混合成员模型，矩阵分析模型及其概括性背景下的最新发展。本教程将提供模型，推理/学习方法和应用程序的平衡组合。 |
| 课程简介: | Graphical models for large scale data mining constitute an exciting development in statistical data analysis which has gained significant momentum in the past decade. Unlike traditional statistical models which often make `i.i.d.' assumptions, graphical models acknowledge dependencies among variables of interest and investigate inference/prediction while taking into account such dependencies. In recent years, latent variable Bayesian networks, such as latent Dirichlet allocation, stochastic block models, Bayesian co-clustering, and probabilistic matrix factorization techniques have achieved unprecedented success in a variety of application domains including topic modeling and text mining, recommendation systems, multi-relational data analysis, etc. The tutorial will give a broad overview of graphical models, and discuss recent developments in the context of mixed-membership models, matrix analysis models, and their generalizations. The tutorial will present a balanced mix of models, inference/learning methods, and applications. |
| 关 键 词: | 统计模型; 贝叶斯网络 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-11-08：zyk |
| 最后编审: | 2020-11-08：zyk |
| 阅读次数: | 61 |