The book presents an axiomatic approach to the problems of
prediction, classification, and statistical learning. Using
methodologies from axiomatic decision theory, and, in particular,
the authors' case-based decision theory, the present studies
attempt to ask what inductive conclusions can be derived from
existing databases. It is shown that simple consistency rules lead
to similarity-weighted aggregation, akin to kernel-based methods.
It is suggested that the similarity function be estimated from the
data. The incorporation of rule-based reasoning is discussed.
General
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