Training data recycling for multi-level learning

Abstract

Among ensemble learning methods, stacking with a meta-level classifier is frequently adopted to fuse the output of multiple base-level classifiers and generate a final score. Labeled data is usually split for base-training and meta-training, so that the meta-level learning is not impacted by over-fitting of base level classifiers on their training data. We… (More)

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Cite this paper

@article{Liu2012TrainingDR, title={Training data recycling for multi-level learning}, author={Jingchen Liu and Scott McCloskey and Yanxi Liu}, journal={Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012)}, year={2012}, pages={2314-2318} }