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- W1508045687 abstract "The 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 identical nor independent An emerging research area, Statistical Relational Learning(SRL), attempts to represent, model, and learn in relational domain Currently, SRL is still at a primitive stage in Canada, which motivates us to conduct this survey as an attempt to raise more attention to this field Our survey presents a brief introduction to SRL and a comparison with conventional learning approaches In this survey we review four SRL models(PRMs, MLNs, RDNs, and BLPs) and compare them theoretically with respect to their representation, structure learning, parameter learning, and inference methods We conclude with a discussion on limitations of current methods." @default.
- W1508045687 created "2016-06-24" @default.
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- W1508045687 date "2010-01-01" @default.
- W1508045687 modified "2023-10-16" @default.
- W1508045687 title "A Survey on Statistical Relational Learning" @default.
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- W1508045687 doi "https://doi.org/10.1007/978-3-642-13059-5_25" @default.
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