Data mining can be regarded as a collection of methods for drawing
inferences from data. The aims of data mining, and some of its
methods, overlap with those of classical statistics. However, there
are some philosophical and methodological differences. We examine
these differences, and we describe three approaches to machine learning
that have developed largely independently: classical statistics,
Vapnik's statistical learning theory, and computational learning
theory. Comparing these approaches, we conclude that statisticians and
data miners can profit by studying each other's methods and using a
judiciously chosen combination of them.
By: Jonathan R. M. Hosking, Edwin P. D. Pednault, Madhu Sudan
Published in: RC20856 in 1997
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