Encyclopedia of Biometrics

2009 Edition
| Editors: Stan Z. Li, Anil Jain

Incremental Learning

Reference work entry
DOI: https://doi.org/10.1007/978-0-387-73003-5_304



Incremental learning is a machine learning paradigm where the learning process takes place whenever new example(s) emerge and adjusts what has been learned according to the new example(s). The most prominent difference of incremental learning from traditional machine learning is that it does not assume the availability of a sufficient training set before the learning process, but the training examples appear over time.


For a long time in the history of machine leaning, there has been an implicit assumption that a “good” training set in a domain is available a priori. The training set is so “good” that it contains all necessary knowledge that once learned, can be reliably applied to any new examples in the domain. Consequently, emphasis is put on learning as much as possible from a fixed training set. Unfortunately, many real-world applications cannot match this ideal case, such as in dynamic control...
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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Deakin UniversityVICAustralia