deep belief networks (DBNs)
Deep Logic Networks: Inserting and Extracting Knowledge from Deep Belief Networks
... in deep networks has been investigated ...for deep networks using confidence rules has been proposed, which combines symbolic representation and quantitative ...from Deep Belief ...
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Deep Belief Networks Using Convolution Neural Networks Algorithm
... of deep learning is not new to higher educatio n. However, deep learning has drawn more attention in recent years as institutions attempt to tap their student’s full learning ...of deep learning, ...
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Convolutional Neural Networks and Deep Belief Networks for Analysing Imbalanced Class Issue in Handwritten Dataset
... towards deep learning algorithms. Deep learning is an example of machine learning collection that is recently introduced to solve complex, high-level abstract and heterogeneous data sets, especially image ...
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Application of Deep Belief Networks for Image Compression
... Deep Belief Networks can be considered as a continuous sequence of layers with each layer made up of Restrictive Boltzmann ...graph. Deep belief networks are used for image ...
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Hybrid Deep Belief Networks for Semi supervised Sentiment Classification
... hybrid deep belief networks (HDBN), to address the semi-supervised sentiment classification problem with deep ...CRBM deep architecture, the bottom layers are constructed by RBM, and ...
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The Research for the Evaluation of Cultivated Land Quality Based on Deep Belief Networks
... Abstract. Traditional evaluation methods of cultivated land quality are mainly on the basis of empirical judgments in the process of weight calculation and membership determination. In this paper, taking Enshi city as an ...
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Retrieval Term Prediction Using Deep Belief Networks
... Our objective is to develop various domain- specific information retrieval support systems that can predict suitable retrieval terms from rele- vant/surrounding words or descriptive texts in Japanese. To our knowledge, ...
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A deep learning method for pathological voice detection using convolutional deep belief networks
... While deep learning techniques have achieved significant progress in the speech recognition field there has been less research work in the area of pathological voice disorders ...Convolutional deep ...
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Learning and Classification of Maneuver Behaviors Based on Deep Belief Networks
... In radar data processing, in order to make full use of the information of labeled data, the output characteristics of deep belief network can be directly mapped to the tag layer, and a recognition model ...
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Automatic dysfluency detection in dysarthric speech using deep belief networks
... using deep neural networks (DNNs) [7], obtaining 3% improvements over Gaussian mixture models (GMMs) ...features, deep learning has shown considerable improvements across several areas of speech ...
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Deep Learning using Restricted Boltzmann Machines
... many deep multi- layer architectures like Deep Belief networks (DBN) and Deep Boltzmann Machines ...neural networks with a few hidden layers, Support Vector Machines (SVM), ...
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Reducing labeled data usage in duplicate detection using deep belief networks
... The first thing noticeable about these results is the fact that the significant differences between semi-supervised and supervised only seem to occur on dataset C. In addition, without extra features semi-supervised ...
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Domain Adaptation for Syntactic and Semantic Dependency Parsing Using Deep Belief Networks
... Previous work have shown that using word clus- ters to replace the sparse lexicalized features (Koo et al., 2008; Turian et al., 2010), helps relieve the performance degradation on the target domain. But for syntactic ...
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Performance evaluation of deep feature learning for RGB-D image/video classification
... Neural Networks for image/video classification have obtained much success in various computer vision ...Existing deep learning al- gorithms are widely used on RGB image or video ...how deep learning ...
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Short term Wind Energy Prediction Algorithm Based on SAGA DBNs
... the deep conviction network and improve the prediction accuracy of wind energy further, this paper proposes a new algorithm which combined the simulated annealing genetic algorithm and the deep ...
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PROPOSED MODELS OF ADAPTIVE KNOWLEDGE AGGREGATOR
... Our research is inspired by a research paper of Sukhbaatar et al. [4], where the goal is to re- construct human motions using two-layer DBN which is called Temporal Deep Belief Networks (TDBN). The ...
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RULES REDUCTION AND OPTIMIZATION OF FUZZY LOGIC MEMBERSHIP FUNCTIONS FOR INDUCTION MOTOR SPEED CONTROLLER
... by deep networks and disparity and compare several techniques for finding such ...on Deep Belief Networks and Stack de-noising Auto-Encoders that are trained on some machine learning ...
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In All Likelihood, Deep Belief Is Not Enough
... by deep belief ...of deep belief networks which is computationally tractable and simple to apply in ...a deep belief network for natural image patches and compare its ...
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Impact of Earnings per Share on Market Price of Share with Special Reference to Selected Companies Listed on NSE
... of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervised feature learning and hierarchical feature ...Various deep learning architectures such as ...
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Deep learning approaches to aircraft maintenance, repair and overhaul: a review
... uses deep CNN to estimate RUL and fault diagnosis of aircraft turbofan ...Multi-objective deep belief networks ensemble [21] and Time window-based NN ...RNN, Deep Neural Network (DNN) ...
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