A Generic Approach to Topic Models

被引：0
|
作者：
Heinrich, Gregor [1 ]
机构：
[1] Univ Leipzig, Fraunhofer IGD, Darmstadt, Germany
来源：
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, PT I | 2009年 / 5781卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This article contributes a generic model of topic models. To define the problem space, general characteristics for this class of models are derived, which ,live rise to a representation of topic models as "mixture networks", a domain-specific compact alternative to Bayesian networks. Besides illustrating the inter-connection Of mixtures in topic models, the benefit of this representation is its straight-forward mapping to inference equations and algorithms, which is shown with the derivation and implementation of a generic Gibbs sampling algorithm.
引用
收藏
页码：517 / 532
页数：16
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