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NeuroCOLT
Technical Report NC-TR-97-029
On Bayesian Case
Matching
Petri
Kontkanen, Petri Myllymaki, Tom Silander and Henry Tirri
University of Helsinki
Finland
Abstract
In this paper we present a new probabilist formalization of the case-based
reasoning paradigm. In contrast to earlier Bayesian approaches, the
new formalization does not need a transformation step between the
original case space and the distribution space. We concentrate on
applying this Bayesian framework to the case matching problem, and
propose a probabilistic scoring metric for this task. In the experimental
part of the paper, the Bayesian case matching score is evaluated empirically
by using publicly available real-world case bases. The results show
that when encountered with cases where some of the feature values
have been removed, a relatively small number of remaining values is
sufficient for retrieving the original case from the case base by
using the proposed measure. The experiments also show that the approach
is computationally very efficient.
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