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We propose a new approach for leave-one-out crossvalidation of neural network classifiers called "crossvalidation with active pattern selection" (CV/APS). In CV/APS, the contribution of the training patterns to back-propagation learning is estimated and this information is used for active selection of CV patterns.On two artificial examples, the computational cost of CV can be reduced to 25% of the normal costs with only small o r no errors.