In situ learning for underwater object recognition
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AbstractWe propose a principled in situ learning framework that is appropriate for a Bayesian classifier implemented with semi-supervised and multi-task learning. We will investigate several different forms of in situ learning, and will perform testing on measured data to help define which is most appropriate for Navy sensing missions. In addition, we will develop new techniques for feature adaptivity and selection, to tune the features to the particular targets and clutter in the environment under test.
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