| dbo:abstract |
Statistical relational learning (SRL) is a subdiscipline of artificial intelligence and machine learning that is concerned with domain models that exhibit both uncertainty (which can be dealt with using statistical methods) and complex, relational structure.Note that SRL is sometimes called Relational Machine Learning (RML) in the literature. Typically, the knowledge representation formalisms developed in SRL use (a subset of) first-order logic to describe relational properties of a domain in a general manner (universal quantification) and draw upon probabilistic graphical models (such as Bayesian networks or Markov networks) to model the uncertainty; some also build upon the methods of inductive logic programming. Significant contributions to the field have been made since the late 1990s. As is evident from the characterization above, the field is not strictly limited to learning aspects; it is equally concerned with reasoning (specifically probabilistic inference) and knowledge representation. Therefore, alternative terms that reflect the main foci of the field include statistical relational learning and reasoning (emphasizing the importance of reasoning) and first-order probabilistic languages (emphasizing the key properties of the languages with which models are represented). (en) Statistical relational learning (SRL) は人工知能、機械学習のサブ分野である。SRLでは、（統計的理論で扱うことのできる）不確実性、および、複雑な関係構造のどちらもを提示するドメインに関するものである。SRLは時として関係性機械学習 (Relational Machine Learning; MRL) と記述されることもある。典型的には、SRLにおいて公知される知識表現形式は一階述語論理（のサブセット）を一般的方法でドメインの関係特性を記述するために使用し、ベイジアンネットワークやマルコフネットワークといったグラフィカルモデルを、不確実性のモデルとして使用する。 (ja) |
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http://www.cs.ubc.ca/~hkhosrav/pub/survey.pdf http://www.jair.org/media/3659/live-3659-6589-jair.pdf ftp://nozdr.ru/biblio/kolxo3/Cs/CsLn/Inductive%20Logic%20Programming,%2016%20conf.,%20ILP%202006(LNCS4455,%20Springer,%202006)(ISBN%203540738460)(466s).pdf%23page=20 http://www.ai.sri.com/~braz/papers/sci-chapter.pdf |
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Statistical relational learning (SRL) は人工知能、機械学習のサブ分野である。SRLでは、（統計的理論で扱うことのできる）不確実性、および、複雑な関係構造のどちらもを提示するドメインに関するものである。SRLは時として関係性機械学習 (Relational Machine Learning; MRL) と記述されることもある。典型的には、SRLにおいて公知される知識表現形式は一階述語論理（のサブセット）を一般的方法でドメインの関係特性を記述するために使用し、ベイジアンネットワークやマルコフネットワークといったグラフィカルモデルを、不確実性のモデルとして使用する。 (ja) Statistical relational learning (SRL) is a subdiscipline of artificial intelligence and machine learning that is concerned with domain models that exhibit both uncertainty (which can be dealt with using statistical methods) and complex, relational structure.Note that SRL is sometimes called Relational Machine Learning (RML) in the literature. Typically, the knowledge representation formalisms developed in SRL use (a subset of) first-order logic to describe relational properties of a domain in a general manner (universal quantification) and draw upon probabilistic graphical models (such as Bayesian networks or Markov networks) to model the uncertainty; some also build upon the methods of inductive logic programming. Significant contributions to the field have been made since the late 1990s. (en) |
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Statistical relational learning (ja) Statistical relational learning (en) |
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