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By embedding prior knowledge into the learning structure, this paper presents a two-step semi-supervised learning method called fuzzy deep belief networks (FDBN) for sentiment classification.
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Enhanced phenomenological consistency and immunity against overfitting of the MLP model were achieved by embedding specific domain prior knowledge.
In this, the robustness against the speckles was increased by embedding the anatomical knowledge into its structure.
Variability data for a population can be obtained by prior knowledge or by a pilot survey.
All knowledge is based on prior knowledge, and by making prior knowledge less available, innovation is impeded.
Unexpectedly, effects of learner control were not moderated by students' prior knowledge.
This work addresses the GRN inference problem by modeling prior knowledge about the network topology.
Yet this problem can be managed every day by using prior knowledge profitably.
The noise tolerance can be increased by including prior knowledge.
Noise is constrained by including prior knowledge of image smoothness.
Furthermore, the facial landmarks are tracked by utilizing prior knowledge on the facial feature configurations.
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