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Statistical Relational Learning
2014This chapter presents background on SRL models on which our work is based on. We start with a brief technical background on first-order logic and graphical models. In Sect. 2.2, we present an overview of SRL models followed by details on two popular SRL models.
Sriraam Natarajan +3 more
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Statistical Relational Learning
Proceedings of the 36th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, 2017Machine learning and database approaches to structured probabilistic models share many commonalities, yet exhibit certain important differences. Machine learning methods focus on learning probabilistic models from (certain) data and efficient learning and inference, whereas probabilistic database approaches focus on storing and efficiently querying ...
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Learning statistical models from relational data
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data, 2011Statistical Relational Learning (SRL) is a subarea of machine learning which combines elements from statistical and probabilistic modeling with languages which support structured data representations. In this survey, we will: 1) provide an introduction to SRL, 2) describe some of the distinguishing characteristics of SRL systems, including relational ...
Lise Getoor, Lilyana Mihalkova
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Stream Mining Using Statistical Relational Learning
2014 IEEE International Conference on Data Mining, 2014Stream mining has gained popularity in recent years due to the availability of numerous data streams from sources such as social media and sensor networks. Data mining on such continuous streams possess a variety of challenges including concept drift and unbounded stream length.
Swarup Chandra +4 more
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Boosting Statistical Relational Learning in Action
2014In the previous chapters, we discussed the structure learning algorithms for two SRL models and extended them to learn with missing data. In this chapter, we discuss how this algorithm can be adapted to learn to act in sequential domains. We then present three of our most successful applications in real health care data—two cardiovascular prediction ...
Sriraam Natarajan +3 more
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Tutorial on Statistical Relational Learning
2005Statistical machine learning is in the midst of a “relational revolution”. After many decades of focusing on independent and identically-distributed (iid) examples, many researchers are now studying problems in which the examples are linked together into complex networks.
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Statistical Relational Learning for Handwriting Recognition
2015We introduce a novel application of handwriting recognition for Statistical Relational Learning. The proposed framework captures the intrinsic structure of handwriting by modeling fundamental character shape representations and their relationships using first-order logic.
Arti Shivram +3 more
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Introduction to Statistical Relational Learning
2007Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different proposed approaches, and applications. Handling inherent uncertainty and exploiting compositional structure are fundamental to ...
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A Survey on Statistical Relational Learning
2010The vast majority of work in Machine Learning has focused on propositional data which is assumed to be identically and independently distributed, however, many real world datasets are relational and most real world applications are characterized by the presence of uncertainty and complex relational structure where the data distribution is neither ...
Hassan Khosravi, Bahareh Bina
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