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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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Social networks and statistical relational learning: a survey
International Journal of Social Network Mining, 2012One of the most appreciated functionality of computers nowadays is their being a means for communication and information sharing among people. With the spread of the internet, several complex interactions have taken place among people, giving rise to huge information networks based on these interactions.
Floriana Esposito +3 more
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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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Statistical learning abilities and their relation to language
Language and Linguistics Compass, 2019Abstract Numerous studies on statistical learning (SL) have demonstrated humans' sensitivity to complex statistical properties in their sensory environment. These observations have had a profound impact on the study of language, highlighting statistical aspects of the linguistic input that can be learned from experience, leading to ...
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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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Parameter and Structure Learning Algorithms for Statistical Relational Learning. [PDF]
, 2012 My research activity focuses on the field of Machine Learning. Two key challenges in most machine learning applications are uncertainty and complexity. The standard framework for handling uncertainty is probability, for complexity is first-order logic. Thus we would like to be able to learn and perform inference in representation languages that combine
BELLODI, Elena, RIGUZZI, Fabrizio
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Learning by extended statistical queries and its relation to PAC learning
1995PAC learning from examples is factored so that (i) the membership queries are used to evaluate empirically “statistical queries” — certain expectations of functionals involving the unknown target. (ii) approximate value of these statistical queries are used to compute an output — an approximation of the target.
Eli Shamir 0001, Clara Shwartzman
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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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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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