Abstract
We consider the average error rate of classification as a function of the number of training examples. We investigate the upper and lower bounds of this error in the class of commonly used algorithms based on inductive learning from examples. As a result we arrive at the astonishing conclusion, that, contrary to what one could expect, the error rate of some algorithms does not decrease monotonically with number of training examples; it rather, initially increases up to a certain point and then it starts to decrease. Furthermore, the classification quality of some algorithms is as poor as that of a naive algorithm. We show that for simple monomials, even if we take an exponentially large training data set, the classification quality of some methods will not be better than if we took just one or several training examples.
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Ejdys, P., Góra, G. (1999). The more we learn the less we know? On inductive learning from examples. In: Raś, Z.W., Skowron, A. (eds) Foundations of Intelligent Systems. ISMIS 1999. Lecture Notes in Computer Science, vol 1609. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0095112
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DOI: https://doi.org/10.1007/BFb0095112
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