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Robust Models in Information Retrieval
2011
2011 22nd International Workshop on Database and Expert Systems Applications
Classification tasks in information retrieval deal with document collections of enormous size, which makes the ratio between the document set underlying the learning process and the set of unseen documents very small. With a ratio close to zero, the evaluation of a model-classifier-combination's generalization ability with leave-n-out-methods or cross-validation becomes unreliable: The generalization error of a complex model (with a more complex hypothesis structure) might be underestimated
doi:10.1109/dexa.2011.73
dblp:conf/dexaw/LipkaS11
fatcat:2vtjpybstfhjpntx3trmbtmquy