PMID: 15344404Sep 4, 2004Paper

An incremental learning algorithm with confidence estimation for automated identification of NDE signals

IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
R PolikarVasant Honavar

Abstract

An incremental learning algorithm is introduced for learning new information from additional data that may later become available, after a classifier has already been trained using a previously available database. The proposed algorithm is capable of incrementally learning new information without forgetting previously acquired knowledge and without requiring access to the original database, even when new data include examples of previously unseen classes. Scenarios requiring such a learning algorithm are encountered often in nondestructive evaluation (NDE) in which large volumes of data are collected in batches over a period of time, and new defect types may become available in subsequent databases. The algorithm, named Learn++, takes advantage of synergistic generalization performance of an ensemble of classifiers in which each classifier is trained with a strategically chosen subset of the training databases that subsequently become available. The ensemble of classifiers then is combined through a weighted majority voting procedure. Learn++ is independent of the specific classifier(s) comprising the ensemble, and hence may be used with any supervised learning algorithm. The voting procedure also allows Learn++ to estimate the...Continue Reading

References

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Feb 5, 2008·IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control·R PolikarT Taylor
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Citations

Apr 10, 2007·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Devi Parikh, Robi Polikar

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