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Deep belief networks

Retrieval Term Prediction Using Deep Belief Networks

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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Deep Logic Networks: Inserting and Extracting Knowledge from Deep Belief Networks

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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Hybrid Deep Belief Networks for Semi supervised Sentiment Classification

Hybrid Deep Belief Networks for Semi supervised Sentiment Classification

... 2006), deep architecture, which composed of multiple levels of non-linear operations, is expected to perform well in semi-supervised learning because of its capability of modeling hard artificial intelligent ...

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Automated fault detection using deep belief networks for the quality inspection of electromotors

Automated fault detection using deep belief networks for the quality inspection of electromotors

... Abstract: Vibration inspection of electro-mechanical components and systems is an important tool for automated reliable online as well as post-process production quality assurance. Considering that bad electromotor ...

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Application of Deep Belief Networks for Image          Compression

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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Convolutional Neural Networks and Deep Belief Networks for Analysing Imbalanced Class Issue in Handwritten Dataset

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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Deep Belief Networks Using Convolution Neural Networks Algorithm

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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The Research for the Evaluation of Cultivated Land Quality Based on Deep Belief Networks

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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Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets

Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets

... other deep learning and machine learning algorithms for imbalanced class datasets in a binomial ...other deep learning and machine learning algorithms for 3 out of 5 imbalanced class ...other deep ...

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Reducing labeled data usage in duplicate detection using deep belief networks

Reducing labeled data usage in duplicate detection using deep belief networks

... For the matching algorithms there is no difference on one of the datasets, dataset R. On the other dataset, dataset C, the additional features improve the matching quality (e.g. improving the F1-score from 0.872 to 0.899 ...

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Domain Adaptation for Syntactic and Semantic Dependency Parsing Using Deep Belief Networks

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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A deep learning method for pathological voice detection using convolutional deep belief networks

A deep learning method for pathological voice detection using convolutional deep belief networks

... In deep learning structures, a region of weight-space is found by generative model and helps the network to converge to global minimum rapidly. Convolutional Restricted Boltzmann Machine (CRBM) is a typical ...

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Automatic dysfluency detection in dysarthric speech using deep belief networks

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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Learning and Classification of Maneuver Behaviors Based on Deep Belief Networks

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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A deep learning method for pathological voice detection using convolutional deep belief networks

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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Sparse deep belief net model for visual area V2

Sparse deep belief net model for visual area V2

... Motivated in part by the hierarchical organization of the cortex, a number of al- gorithms have recently been proposed that try to learn hierarchical, or “deep,” structure from unlabeled data. While several ...

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Speaker recognition with hybrid features from a deep belief network

Speaker recognition with hybrid features from a deep belief network

... Machines, Deep Belief Networks, the contrastive divergence algorithm used for training them and the supervised Support Vector Machine classifica- ...

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Multi-layer neural network with deep belief network for gearbox fault diagnosis

Multi-layer neural network with deep belief network for gearbox fault diagnosis

... neural networks, typically used in supervised learning to make a prediction or ...neural networks (starting from random initialization) gets easily stuck in “apparent local minima or ...2006, deep ...

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Isomap and Deep Belief Network-Based Machine Health Combined Assessment Model

Isomap and Deep Belief Network-Based Machine Health Combined Assessment Model

... various deep learning algorithms, such as deep belief networks (DBNs) [9], convolutional neural network [10] and deep neural networks (DNNs) [11] have been applied successfully ...

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Intrusion detection for IoT based on improved genetic algorithm and deep belief network

Intrusion detection for IoT based on improved genetic algorithm and deep belief network

... a deep belief networks, GA performs multiple iterations to produce an optimal network structure, DBN then uses the obtained network structure as an intrusion detection model to classify the ...using ...

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