Learn++: A classifier independent incremental learning algorithm for supervised neural networks

Robi Polikar, Jeff Byorick, Stefan Krause, Anthony Marino, Michael Moreton

Research output: Contribution to conferencePaperpeer-review

48 Scopus citations

Abstract

A versatile incremental learning algorithm is introduced for supervised neural network type classifiers. The proposed algorithm, called Learn++, exploits the synergistic expressive power of an ensemble of weak classifiers for learning additional information from new data. Learn++ is capable of learning new classes, without forgetting previously acquired knowledge, even when the previously used data is no longer available. Furthermore, Learn++ is independent of the specific type of the classifier, and adds the incremental learning capability to any supervised neural network classifier.

Original languageEnglish (US)
Pages1742-1747
Number of pages6
StatePublished - Jan 1 2002
Event2002 International Joint Conference on Neural Networks (IJCNN '02) - Honolulu, HI, United States
Duration: May 12 2002May 17 2002

Other

Other2002 International Joint Conference on Neural Networks (IJCNN '02)
CountryUnited States
CityHonolulu, HI
Period5/12/025/17/02

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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