Abstract
This paper presents both a theoretical discussion and an experimental comparison of batch and incremental learning in an attempt to individuate some of the respective advantages and disadvantages of the two approaches when learning from frequently updated databases. The paper claims that incremental learning might be more suitable for this purpose, although a number of issues remain to be resolved.
Cite
CITATION STYLE
Carbonara, L., & Borrowman, A. (1998). A comparison of batch and incremental supervised learning algorithms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1510, pp. 264–272). Springer Verlag. https://doi.org/10.1007/bfb0094828
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