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- W2490893199 abstract "Deep neural networks are known for their capabilities for automatic feature learning from data. For this reason, previous research has tended to interpret deep learning techniques as data-driven methods, while few advances have been made from knowledge-driven perspectives. We propose to design a semantic rich deep learning model from a knowledge driven perspective, by introducing formal semantics into deep learning process. We propose ontology-based deep restricted Boltzmann machine (OB-DRBM), in which we use ontology to guide architecture design of deep restricted Boltzmann machines (DRBM), as well as to assist in their training and validation processes. Our model learns a set of related semantic-rich data representations from both formal semantics and data distribution. Representations in this set correspond to concepts at various semantic levels in a domain ontology. We show that our model leads to an improved performance, when compared with conventional deep learning models in classification tasks." @default.
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- W2490893199 date "2016-01-01" @default.
- W2490893199 modified "2023-10-12" @default.
- W2490893199 title "Ontology-Based Deep Restricted Boltzmann Machine" @default.
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- W2490893199 doi "https://doi.org/10.1007/978-3-319-44403-1_27" @default.
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