Neural networks and the bias/variance dilemma
Neural Computation
Dependency Networks for Relational Data
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Leveraging Relational Autocorrelation with Latent Group Models
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
A bias/variance decomposition for models using collective inference
Machine Learning
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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Bias/variance analysis [1] 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 with an underlying assumption that there is no variation in model predictions due to the inference process used for prediction. This assumption is often violated when collective inference models are used for classification of relational data. In relational data, when there are dependencies among the class labels of related instances, the inferences about one object can be used to improve the inferences about other related objects. Collective inference techniques exploit these dependencies by jointly inferring the class labels in a test set. This approach can produce more accurate predictions than conditional inference for each instance independently, but it also introduces an additional source of error, both through the use of approximate inference algorithms and through variation in the availability of test set information. To date, the impact of inference error on relational model performance has not been investigated.