%A Peter Turney
%T Exploiting context when learning to classify
%X This paper addresses the problem of classifying observations when
features are context-sensitive, specifically 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
two 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 problem is to recognize
words spoken by a new speaker, not represented in the training set. For both
domains, exploiting context results in substantially more accurate classification.
%D 1993
%K context, robust classification, context-sensitive features, machine learning, robust learning.
%P 402-407
%L cogprints1863

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