Autocorrelation and Relational Learning: Challenges and Opportunities
作者： Jennifer Neville , Ozgur Simsek , David Jensen
DOI: 10.21236/ADA472226
关键词: Machine learning 、 Mathematics 、 Independence (mathematical logic) 、 Artificial intelligence 、 Inference 、 Model learning 、 Variable (computer science) 、 Statistical relational learning 、 Autocorrelation
摘要: Abstract : Autocorrelation, a common characteristic of many datasets, refers to correlation between values the same variable on related objects. It violates critical assumption instance independence that underlies most conventional models. In this paper, we provide an overview research autocorrelation in number fields with emphasis implications for relational learning, and outline challenges opportunities model learning inference.
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