Operations for Learning with Graphical Models
Operations for Learning with Graphical Models
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DOI：
10.1613/jair.62
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发表时间：
1994-01-01
影响因子：
5
通讯作者：
Buntine, Wray L.
中科院分区：
文献类型：
--
作者：
Buntine, Wray L.
本文是从图模型角度对实证统计学习进行的多学科回顾。图模型的著名示例包括贝叶斯网络、表示马尔可夫链的有向图以及表示马尔可夫场的无向网络。这些图形模型扩展到使用板符号的模型数据分析和实证学习。提供了用于简化和处理问题的图形操作，包括分解、微分以及指数族概率模型的处理。在图形框架中回顾了两种标准的学习算法模式：吉布斯采样和期望最大化算法。使用这些操作和模式，可以从其图形规范合成一些流行的算法。这包括线性回归的版本、前馈网络技术以及从数据中学习高斯和离散贝叶斯网络。本文最后概述了数据分析的一些含义，并总结了一些流行算法如何落入所提出的框架内。这里的主要原创贡献是分解技术以及图形模型为理解和开发复杂学习算法提供框架的演示。
This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, and the manipulation of probability models from the exponential family. Two standard algorithm schemas for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximization algorithm. Using these operations and schemas, some popular algorithms can be synthesized from their graphical specification. This includes versions of linear regression, techniques for feed-forward networks, and learning Gaussian and discrete Bayesian networks from data. The paper concludes by sketching some implications for data analysis and summarizing how some popular algorithms fall within the framework presented.The main original contributions here are the decomposition techniques and the demonstration that graphical models provide a framework for understanding and developing complex learning algorithms.