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The Nature of Statistical Learning Theory
, 1999
"... Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based on the deve ..."
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Cited by 12976 (32 self)
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Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based
Research on Statistical Relational Learning
"... This paper presents an overview of the research on learning statistical models of relational data being carried out at the University of Washington. Our work falls into five main directions: learning models of social networks; learning models of sequential relational processes; scaling up stati ..."
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This paper presents an overview of the research on learning statistical models of relational data being carried out at the University of Washington. Our work falls into five main directions: learning models of social networks; learning models of sequential relational processes; scaling up
Statistical relational learning for link prediction
 In Proceedings of the Workshop on Learning Statistical Models from Relational Data at IJCAI2003
, 2003
"... Link prediction is a complex, inherently relational, task. Be it in the domain of scientific citations, social networks or hypertext links, the underlying data are extremely noisy and the characteristics useful for prediction are not readily available in a “flat ” file format, but rather involve com ..."
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Cited by 87 (6 self)
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complex relationships among objects. In this paper, we propose the application of our methodology for Statistical Relational Learning to building link prediction models. We propose an integrated approach to building regression models from data stored in relational databases in which potential predictors
Statistical Relational Learning a Logical Introduction
"... Abstract. Statistical Relational Learning is a new subfield of artificial intelligence ..."
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Abstract. Statistical Relational Learning is a new subfield of artificial intelligence
Contextbased statistical relational learning
"... The relational structure is an important source of information, which is often ignored by the traditional statistical learning methods. Thus this thesis focuses on how to explicitly exploit such relational information in statistical learning tasks so as to build more effective and more robust models ..."
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The relational structure is an important source of information, which is often ignored by the traditional statistical learning methods. Thus this thesis focuses on how to explicitly exploit such relational information in statistical learning tasks so as to build more effective and more robust
Change of representation for statistical relational learning
 Proc. IJCAI’07
, 2007
"... Statistical relational learning (SRL) algorithms learn statistical models from relational data, such as that stored in a relational database. We previously introduced view learning for SRL, in which the view of a relational database can be automatically modified, yielding more accurate statistical m ..."
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Cited by 20 (5 self)
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Statistical relational learning (SRL) algorithms learn statistical models from relational data, such as that stored in a relational database. We previously introduced view learning for SRL, in which the view of a relational database can be automatically modified, yielding more accurate statistical
Statistical Relational Learning with Soft Quantifiers
"... Abstract. Quantification in statistical relational learning (SRL) is either existential or universal, however humans might be more inclined to express knowledge using soft quantifiers, such as “most ” and “a few”. In this paper, we define the syntax and semantics of PSLQ, a new SRL framework that s ..."
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Abstract. Quantification in statistical relational learning (SRL) is either existential or universal, however humans might be more inclined to express knowledge using soft quantifiers, such as “most ” and “a few”. In this paper, we define the syntax and semantics of PSLQ, a new SRL framework
Statistical Relational Learning at U Penn
"... We do statistical relational learning by incrementally extracting data from a relational database, and computing features of that data which are then used in a classical discriminative statistical model component. Candidate features for the model are generated by a structured search in the space of ..."
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We do statistical relational learning by incrementally extracting data from a relational database, and computing features of that data which are then used in a classical discriminative statistical model component. Candidate features for the model are generated by a structured search in the space
Towards Multistrategic Statistical Relational Learning
"... Abstract Statistical Relational Learning (SRL) is a growing field in Machine Learning that aims at the integration of logicbased learning approaches with probabilistic graphical models. Markov Logic Networks (MLNs) are one of the stateoftheart SRL models that combine firstorder logic and Markov ..."
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Abstract Statistical Relational Learning (SRL) is a growing field in Machine Learning that aims at the integration of logicbased learning approaches with probabilistic graphical models. Markov Logic Networks (MLNs) are one of the stateoftheart SRL models that combine firstorder logic
Statistical Relational Learning for Document Mining
, 2003
"... A major obstacle to fully integrated deployment of statistical learners is the assumption that data sits in a single table, even though most realworld databases have complex relational structures. In this paper, we introduce an integrated approach to building regression models from data stored ..."
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Cited by 41 (5 self)
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A major obstacle to fully integrated deployment of statistical learners is the assumption that data sits in a single table, even though most realworld databases have complex relational structures. In this paper, we introduce an integrated approach to building regression models from data
Results 1  10
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3,832,494