  This paper addresses the problem of classifying observations when features
are context-sensitive, especially when the testing set involves a context that
is different from the training set. The paper begins with a precise definition
of the problem, then general strategies are presented for enhancing the
performance of classification algorithms on this type of problem. These
strategies are tested on three domains. The first domain is the diagnosis of
gas turbine engines. The problem is to diagnose a faulty engine in one context,
such as warm weather, when the fault has previously been seen only in another
context, such as cold weather. The second domain is speech recognition. The
context is given by the identity of the speaker. The problem is to recognize
words spoken by a new speaker, not represented in the training set. The third
domain is medical prognosis. The problem is to predict whether a patient with
hepatitis will live or die. The context is the age of the patient. For all
three domains, exploiting context results in substantially more accurate
classification.

Machine Learning;Computer Vision and Pattern Recognition