A bias/variance decomposition for models using collective inference

  title={A bias/variance decomposition for models using collective inference},
  author={Jennifer Neville and David D. Jensen},
  journal={Machine Learning},
Bias/variance analysis is a useful tool for investigating the performance of machine learning algorithms. Conventional analysis decomposes loss into errors due to aspects of the learning process, but in relational domains, the inference process used for prediction introduces an additional source of error. Collective inference techniques introduce additional error, both through the use of approximate inference algorithms and through variation in the availability of test-set information. To date… CONTINUE READING
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