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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Jain, S., Lange, S., & Zilles, S. (2006). Towards a better understanding of incremental learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4264 LNAI, pp. 169–183). Springer Verlag. https://doi.org/10.1007/11894841_16
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