: NLP Vocabulary: deep belief network

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deep belief network

A kind of deep neural network, which is composed of stacked layers of restricted boltzmann machines. It is a generative model used to solve unsupervised learning tasks to reduce the dimensionality of features, and can also be used to solve supervised learning tasks to build classification models or regression models. (Adapted from Liu, Single-point wind forecasting methods based on reinforcement learning, in Wind Forecasting in Railway Engineering, 2021)

Deep belief networks consist of multiple layers of restricted Boltzmann machines (RBMs). (Deselaers, Hasan, Bender & Ney, 2009)
Deep belief networks (DBNs) have been first used in call routing classification (Deoras and Sarikaya 2013). (Li, Li & Qi, 2018)
In this section we propose a deep belief network for modeling the semantic relationship between questions and their answers. (Wang, Wang, Sun, Liu & Sun, 2010)
Our proposed framework consists of deep belief networks (DBN) for each language and we employ canonical correlation analysis (CCA) to construct a shared semantic space. (Kim, Nam & Gurevych, 2012)
Rashwan et al. (2015) use deep belief network to build a diacritization model for Arabic that focuses on improving syntactic diacritization and build subclassifiers based on the analysis of a confusion matrix and POS tags. (Alqahtani, Mishra & Diab, 2020)

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