文件名称:
An Introduction to Conditional Random Fields
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文件大小: 675kb
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上传时间: 2015-07-11
详细说明： Often we wish to predict a large number of variables that depend on each other as well as on other observed variables. Structured prediction methods are essentially a combination of classication and graphical modeling, combining the ability of graphical models to compactly model multivariate data with the ability of classication methods to perform prediction using large sets of input features. This tutorial describes conditional random elds, a popular probabilistic method for structured prediction. CRFs have seen wide applicatio n in natural language processing, computer vision, and bioinformatics. We describe methods for inference and parameter estimation for CRFs, including practical issues for implementing large scale CRFs. We do not assume previous knowledge of graphical modeling, so this tutorial is intended to be useful to practitioners in a wide variety of elds. ...展开收缩
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