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DD at a point is a measure of how many instances
from different bags are
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near that point and how far away the negative instances are.
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The algorithm returns point(s) in feature space with
high DD
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For a
single point target concept (t) and positive and negative bags (B), we
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can
find t by maximizing
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If we
assume a uniform prior, and that bags are conditionally independent
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given
the concept, then we maximize likelihood
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Each
bag is made up of many instances
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