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Conditional random fields (CRFs) are a class of statistical modelling method often applied in pattern recognition and machine learning, where they are used for structured prediction. Whereas an ordinary classifier predicts a label for a single sample without regard to "neighboring" samples, a CRF can take context into account; e.g., the linear chain CRF popular in natural language processing predicts sequences of labels for sequences of input samples. CRFs are a type of discriminative undirected probabilistic graphical model. It is used to encode known relationships between observations and construct consistent interpretations. It is often used for labeling or parsing of sequential data, such as natural language text or biological sequences〔 〕 and in computer vision. Specifically, CRFs find applications in shallow parsing, named entity recognition, gene finding and peptide critical functional region finding, among other tasks, being an alternative to the related hidden Markov models (HMMs). In computer vision, CRFs are often used for object recognition and image segmentation. ==Description== Lafferty, McCallum and Pereira〔 define a CRF on observations and random variables as follows: Let be a graph such that What this means is that a CRF is an undirected graphical model whose nodes can be divided into exactly two disjoint sets and , the observed and output variables, respectively; the conditional distribution is then modeled. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「conditional random field」の詳細全文を読む スポンサード リンク
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