Classification of Sets using Restricted Boltzmann Machines
UAI(2011)
摘要
We consider the problem of classification when inputs correspond to sets of
vectors. This setting occurs in many problems such as the classification of
pieces of mail containing several pages, of web sites with several sections or
of images that have been pre-segmented into smaller regions. We propose
generalizations of the restricted Boltzmann machine (RBM) that are appropriate
in this context and explore how to incorporate different assumptions about the
relationship between the input sets and the target class within the RBM. In
experiments on standard multiple-instance learning datasets, we demonstrate the
competitiveness of approaches based on RBMs and apply the proposed variants to
the problem of incoming mail classification.
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关键词
boltzmann machines,classification,sets
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