Towards a better understanding of incremental learning

Authors:
Sanjay Jain;Steffen Lange;Sandra Zilles
Affiliations:
School of Computing, National University of Singapore, Singapore;FB Informatik, Hochschule Darmstadt, Darmstadt;DFKI GmbH, Kaiserslautern
Venue:
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
Year:
2006

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Abstract

The present study aims at insights into the nature of incremental learning in the context of Gold's model of identification in the limit. With a focus on natural requirements such as consistency and conservativeness, incremental learning is analysed both for learning from positive examples and for learning from positive and negative examples. The results obtained illustrate in which way different consistency and conservativeness demands can affect the capabilities of incremental learners. These results may serve as a first step towards characterising the structure of typical classes learnable incrementally and thus towards elaborating uniform incremental learning methods.