introduced the Deep Belief Network
, with multiple layers of Restricted Boltzmann Machines, greedily training one layer at a time in an unsupervised way.
proposed a Deep Belief Network
and Dempster-Shafer- (DBN-DS-) based multiclassifier for the pathologic prediction of prostate cancer, tested with data from thousands of patients, and obtained high accuracy (81.27%) against the Partin table (64.14%).
Firstly, a large number of unlabeled images are selected as training samples, and a 2D deep belief network
(2D-DBN) is used as a classifier structure.
Deep Belief Network
is a type of deep neural network with multiple hidden layers, introduced by Hinton et al.
Third, our study indicates that physical activity alone cannot reduce diabetes disparities between Blacks and Whites based on observational inferences, an inferential approach in Bayesian Belief Network
machine learning modeling.
In our investigations we used a special computer tool called BeliefSEEKER (a belief network
system) that was developed at the University of Information Technology and Management in Rzeszow, Poland, in cooperation with the University of Kansas.
In this section, a novel algorithm based on deep belief network
(DBN) is proposed.
A sampling of topics: supply chain performance measurement using logical aggregation, a Bayesian belief network
modeling of customer behavior on apparel coordination for fashion retailing business, consensus measures for symbolic data, cigarette sensory evaluation classifier predictive control algorithm, uncertainty aversion under distorted probability, and an agent-based approach to modeling small satellite constellations.
The values of some of the belief network
variables are displayed in Table 1.
Deep learning architectures overcome the learning difficulty through stratified training, such as deep belief network
, which pre-train multiple layers from bottom to up to construct the classification model.
This paper takes deep learning as the point of penetration and uses multilayer network architecture to abstract the characteristics of layers and establish a cardiovascular disease prediction model based on deep belief network
. At the same time, the prediction model based on deep trust network is improved by using reconstruction error to achieve better prediction.
Some scholars [24-27] introduce deep autoencoder network and deep belief network
to solve the problem of transformer fault diagnosis.