Autocorrelation and Relational Learning: Challenges and Opportunities

Jennifer Neville, Özgür Şimşek, David Jensen

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

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Abstract

Autocorrelation, a common characteristic of many datasets, refers to correlation between values of the same variable on related objects. It violates the critical assumption of in- stance independence that underlies most conventional models. In this paper, we provide an overview of research on autocorrelation in a number of fields with an emphasis on implications for relational learning, and outline a number of challenges and opportunities for model learning and inference.
Original languageEnglish
Title of host publicationICML 2004 Workshop on Statistical Relational Learning and its Connections to Other Fields
Pages74–81
Number of pages8
Publication statusPublished - 1 Jan 2004

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