Non-IIDness Learning in Behavioral and Social Data

@article{Cao2014NonIIDnessLI,
  title={Non-IIDness Learning in Behavioral and Social Data},
  author={Longbing Cao},
  journal={Comput. J.},
  year={2014},
  volume={57},
  pages={1358-1370}
}
Most of the classic theoretical systems and tools in statistics, data mining and machine learning are built on the fundamental assumption of IIDness, which assumes the independence and identical distribution of underlying objects, attributes and/or values. However, complex behavioral and social problems often exhibit strong couplings and heterogeneity between values, attributes and objects (i.e., non-IIDness). This fundamentally challenges the IIDness-based learning methodologies and techniques… CONTINUE READING
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