Coping with Unconsidered Context in Machine Learning Scenarios
نویسنده
چکیده
The paper transfers previous insights on coping with unconsidered context of formalized knowledge to the field of Machine Learning by translating the two main steps of coping with unconsidered contexts, namely context identification and context classification. For identification, two alternative heuristic methods are suggested. For classification the use of standard Machine Learning methods on a meta-level is suggested. The different options are evaluated in an image classification scenario. We claim that the transfer of strategies for coping with unconsidered context from symbolic reasoning settings to numerical Machine Learning Scenarios shows the universal usefulness of these strategies.
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Coping with Unconsidered Context of Formalized Knowledge
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