  Defining similarity measures is a requirement for some machine learning
methods. One such method is case-based reasoning (CBR) where the similarity
measure is used to retrieve the stored case or set of cases most similar to the
query case. Describing a similarity measure analytically is challenging, even
for domain experts working with CBR experts. However, data sets are typically
gathered as part of constructing a CBR or machine learning system. These
datasets are assumed to contain the features that correctly identify the
solution from the problem features, thus they may also contain the knowledge to
construct or learn such a similarity measure. The main motivation for this work
is to automate the construction of similarity measures using machine learning,
while keeping training time as low as possible. Our objective is to investigate
how to apply machine learning to effectively learn a similarity measure. Such a
learned similarity measure could be used for CBR systems, but also for
clustering data in semi-supervised learning, or one-shot learning tasks. Recent
work has advanced towards this goal, relies on either very long training times
or manually modeling parts of the similarity measure. We created a framework to
help us analyze current methods for learning similarity measures. This analysis
resulted in two novel similarity measure designs. One design using a
pre-trained classifier as basis for a similarity measure. The second design
uses as little modeling as possible while learning the similarity measure from
data and keeping training time low. Both similarity measures were evaluated on
14 different datasets. The evaluation shows that using a classifier as basis
for a similarity measure gives state of the art performance. Finally the
evaluation shows that our fully data-driven similarity measure design
outperforms state of the art methods while keeping training time low.
