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1. Full Text Acoustic Modeling Using Deep Belief Networks
by Mohamed, A and Dahl, G. E and Hinton, G
IEEE transactions on audio, speech, and language processing, ISSN 1558-7916, 01/2012, Volume 20, Issue 1, pp. 14 - 22
Training | Computational modeling | Hidden Markov models | Artificial neural networks | Speech recognition | Acoustic modeling | neural networks | Speech | Data models | deep belief networks (DBNs) | phone recognition | Engineering, Electrical & Electronic | Engineering | Acoustics | Technology | Science & Technology
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2. Full Text Breast cancer classification using deep belief networks
by Abdel-Zaher, Ahmed M and Eldeib, Ayman M
Expert systems with applications, ISSN 0957-4174, 03/2016, Volume 46, pp. 139 - 144
Pattern recognition | Deep learning based classifier | Breast cancer diagnosis | CAD | Classification | Operations Research & Management Science | Engineering | Technology | Computer Science | Engineering, Electrical & Electronic | Computer Science, Artificial Intelligence | Science & Technology | Analysis | Breast cancer | Biomedical engineering | Neural networks | Oncology, Experimental | Diagnosis | Research | Cancer | Accuracy | Back propagation | Breast | Computer aided design | Belief networks
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3. Full Text Augmentable gamma belief networks
by Zhou, Mingyuan and Cong, Yulai and Chen, Bo
Journal of machine learning research, ISSN 1532-4435, 09/2016, Volume 17, pp. 1 - 44
Deep learning | Topic modeling | Bayesian nonparametrics | Multilayer representation | Poisson factor analysis | Unsupervised learning | Automation & Control Systems | Computer Science, Artificial Intelligence | Technology | Computer Science | Science & Technology
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4. Full Text Learning to Diversify Deep Belief Networks for Hyperspectral Image Classification
by Ping Zhong and Zhiqiang Gong and Shutao Li and Schonlieb, Carola-Bibiane
IEEE transactions on geoscience and remote sensing, ISSN 0196-2892, 06/2017, Volume 55, Issue 6, pp. 3516 - 3530
Training | diversity | image classification | Neurons | Hidden Markov models | Deep belief network (DBN) | hyperspectral image | Feature extraction | Hyperspectral imaging | Engineering | Physical Sciences | Technology | Engineering, Electrical & Electronic | Remote Sensing | Imaging Science & Photographic Technology | Science & Technology | Geochemistry & Geophysics | Usage | Research | Artificial neural networks | Back propagation | Procedures | Remote sensing | Belief networks | Tuning | Learning | Neural networks | Samples | Classification | Models | Framework | Image classification
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5. Full Text Deep Belief Networks Based Voice Activity Detection
by Xiao-Lei Zhang and Ji Wu
IEEE transactions on audio, speech, and language processing, ISSN 1558-7916, 04/2013, Volume 21, Issue 4, pp. 697 - 710
Training | Support vector machines | Deep learning | Fuses | information fusion | voice activity detection | Feature extraction | Speech | Acoustics | Speech processing | Engineering, Electrical & Electronic | Engineering | Technology | Science & Technology | Signal and communications theory | Telecommunications and information theory | Exact sciences and technology | Signal, noise | Signal processing | Applied sciences | Information, signal and communications theory | Signal representation. Spectral analysis | Detection, estimation, filtering, equalization, prediction | Information theory
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6. Full Text Spectral-Spatial Classification of Hyperspectral Data Based on Deep Belief Network
by Yushi Chen and Xing Zhao and Xiuping Jia
IEEE journal of selected topics in applied earth observations and remote sensing, ISSN 1939-1404, 06/2015, Volume 8, Issue 6, pp. 2381 - 2392
logistic regression (LR) | restricted Boltzmann machine (RBM) | feature extraction (FE) | hyperspectral data classification | support vector machine (SVM) | Iron | Vectors | Training | Support vector machines | deep learning | Deep belief network (DBN) | Feature extraction | Hyperspectral imaging | Support vector machine (SVM)
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7. Full Text Application of Deep Belief Networks for natural language understanding
by Sarikaya, Ruhi and Hinton, Geoffrey and Deoras, Anoop
IEEE/ACM transactions on audio, speech, and language processing, ISSN 2329-9290, 04/2014, Volume 22, Issue 4, pp. 778 - 784
natural language understanding | DBN | deep learning | RBM | call-routing | deep neural nets | Boosting | Vectors | Deep Learning | Training | Support vector machines | Call-Routing | Hidden Markov models | Speech | Speech processing | Deep Neural Nets | Natural language Understanding | Deep learning | Deep neural nets | Call-routing | Natural language understanding | Engineering, Electrical & Electronic | Engineering | Acoustics | Technology | Science & Technology | Neural networks | Classification
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8. Full Text An approach to fault diagnosis of reciprocating compressor valves using Teager–Kaiser energy operator and deep belief networks
by Tran, Van Tung and AlThobiani, Faisal and Ball, Andrew
Expert systems with applications, ISSN 0957-4174, 07/2014, Volume 41, Issue 9, pp. 4113 - 4122
Fault diagnosis | Deep belief networks | Reciprocating compressor | Teager–Kaiser energy operator | Teager-Kaiser energy operator | Operations Research & Management Science | Engineering | Technology | Computer Science | Engineering, Electrical & Electronic | Computer Science, Artificial Intelligence | Science & Technology | Telecommunications and information theory | Exact sciences and technology | Connectionism. Neural networks | Information, signal and communications theory | Detection, estimation, filtering, equalization, prediction | Computer science; control theory; systems | Industrial metrology. Testing | Signal and communications theory | Mechanical engineering. Machine design | Signal, noise | Applied sciences | General | Artificial intelligence | Valves | Learning | Faults | Vibration | Reciprocating | Classification | Belief networks
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9. Full Text Deep belief network based k-means cluster approach for short-term wind power forecasting
by Wang, Kejun and Qi, Xiaoxia and Liu, Hongda and Song, Jiakang
Energy (Oxford), ISSN 0360-5442, 12/2018, Volume 165, pp. 840 - 852
Deep learning | k-means clustering | Deep belief network | Wind power forecasting | Numerical weather prediction | Thermodynamics | Physical Sciences | Technology | Energy & Fuels | Science & Technology | Weather | Algorithms | Neural networks | Analysis | Green technology | Wind power | Numerical weather forecasting | Cluster analysis | Vector quantization | Alternative energy | Volatility | Weather forecasting | Data processing | Clustering | Back propagation networks | Belief networks | Wave propagation | Wind energy | Renewable energy | Morlet wavelet | Wind power generation | Economic forecasting | Mathematical models
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10. Full Text Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning
by Wenhao Huang and Guojie Song and Haikun Hong and Kunqing Xie
IEEE transactions on intelligent transportation systems, ISSN 1524-9050, 10/2014, Volume 15, Issue 5, pp. 2191 - 2201
Training | Deep learning | multitask learning (MTL) | Roads | traffic flow prediction | Artificial neural networks | task grouping | Predictive models | Vehicles | Traffic flow prediction | Engineering | Transportation Science & Technology | Technology | Engineering, Civil | Transportation | Engineering, Electrical & Electronic | Science & Technology | Intelligent vehicle highway systems | Traffic flow | Learning | Tasks | Architecture | Regression | Mathematical models | Belief networks
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11. Full Text Electric Locomotive Bearing Fault Diagnosis Using a Novel Convolutional Deep Belief Network
by Haidong Shao and Hongkai Jiang and Haizhou Zhang and Tianchen Liang
IEEE transactions on industrial electronics (1982), ISSN 0278-0046, 03/2018, Volume 65, Issue 3, pp. 2727 - 2736
Fault diagnosis | Vibrations | Support vector machines | Convolution | feature learning | Convolutional deep belief network (CDBN) | Machine learning | Feature extraction | electric locomotive bearing | exponential moving average (EMA) | Standards | fault diagnosis | Engineering, Electrical & Electronic | Engineering | Automation & Control Systems | Technology | Instruments & Instrumentation | Science & Technology | Convolutional codes | Measurement | Usage | Vibration | Gaussian processes | Research | Trains | Network reliability | Electric railroads | Performance enhancement | Bearing
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12. Full Text Deep neural network in QSAR studies using deep belief network
by Ghasemi, Fahimeh and Mehridehnavi, Alireza and Fassihi, Afshin and Pérez-Sánchez, Horacio
Applied soft computing, ISSN 1568-4946, 01/2018, Volume 62, pp. 251 - 258
Deep neural network | Drug design | Deep belief network | QSAR | Biological activity prediction | Computer Science, Artificial Intelligence | Computer Science, Interdisciplinary Applications | Technology | Computer Science | Science & Technology | Image processing | Public opinion | Neural networks | Analysis
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13. Full Text Deep belief network based deterministic and probabilistic wind speed forecasting approach
by Wang, H.Z and Wang, G.B and Li, G.Q and Peng, J.C and Liu, Y.T
Applied energy, ISSN 0306-2619, 11/2016, Volume 182, pp. 80 - 93
Deep learning | Wavelet transform | Wind speed forecast | Deep belief network | Quantile regression | Engineering | Engineering, Chemical | Technology | Energy & Fuels | Science & Technology | Case studies | Control engineering | Forecasts and trends
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