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[1] Ricardo J. G. B. Campello,et al. A fuzzy extension of the Rand index and other related indexes for clustering and classification assessment , 2007, Pattern Recognit. Lett..
[2] P. Sachdev,et al. Classifying MCI Subtypes in Community-Dwelling Elderly Using Cross-Sectional and Longitudinal MRI-Based Biomarkers , 2017, Front. Aging Neurosci..
[3] Hyunsoo Lee,et al. Learning framework of multimodal Gaussian-Bernoulli RBM handling real-value input data , 2018, Neurocomputing.
[4] Yoshua Bengio,et al. The Spike-and-Slab RBM and Extensions to Discrete and Sparse Data Distributions , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[5] Mohammad Mehdi Homayounpour,et al. Effective sparsity control in deep belief networks using normal regularization term , 2017, Knowledge and Information Systems.
[6] Huan Liu,et al. An Unsupervised Feature Selection Framework for Social Media Data , 2014, IEEE Transactions on Knowledge and Data Engineering.
[7] Hemant A. Patil,et al. Novel Unsupervised Auditory Filterbank Learning Using Convolutional RBM for Speech Recognition , 2016, IEEE/ACM Transactions on Audio, Speech, and Language Processing.
[8] Sandeep Yadav,et al. Restricted Boltzmann machine and softmax regression for fault detection and classification , 2018 .
[9] Honglak Lee,et al. Sparse deep belief net model for visual area V2 , 2007, NIPS.
[10] Hayat Al-Dmour,et al. A clustering fusion technique for MR brain tissue segmentation , 2018, Neurocomputing.
[11] Na Zhang,et al. Hidden-layer visible deep stacking network optimized by PSO for motor imagery EEG recognition , 2017, Neurocomputing.
[12] Licheng Jiao,et al. Recursive Autoencoders-Based Unsupervised Feature Learning for Hyperspectral Image Classification , 2017, IEEE Geoscience and Remote Sensing Letters.
[13] Ascensión Gallardo-Antolín,et al. Enhancement of a text-independent speaker verification system by using feature combination and parallel structure classifiers , 2018, Neural Computing and Applications.
[14] Stefano Ermon,et al. Label-Free Supervision of Neural Networks with Physics and Domain Knowledge , 2016, AAAI.
[15] Minglun Gong,et al. Multi-modal feature fusion for geographic image annotation , 2018, Pattern Recognit..
[16] Na Lu,et al. A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[17] Gang Chen. Deep Transductive Semi-supervised Maximum Margin Clustering , 2015, ArXiv.
[18] Kenji Doya,et al. Expected energy-based restricted Boltzmann machine for classification , 2015, Neural Networks.
[19] Jiancheng Lv,et al. Finding a good initial configuration of parameters for restricted Boltzmann machine pre-training , 2017, Soft Comput..
[20] Yadong Mu,et al. Supervised deep learning with auxiliary networks , 2014, KDD.
[21] Miao He,et al. Rolling bearing fault severity identification using deep sparse auto-encoder network with noise added sample expansion , 2017 .
[22] R. Real,et al. The Probabilistic Basis of Jaccard's Index of Similarity , 1996 .
[23] Andrew Y. Ng,et al. Parsing Natural Scenes and Natural Language with Recursive Neural Networks , 2011, ICML.
[24] Dario Pompili,et al. Random ensemble learning for EEG classification , 2018, Artif. Intell. Medicine.
[25] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[26] Maqsood Hayat,et al. Efficient computational model for classification of protein localization images using Extended Threshold Adjacency Statistics and Support Vector Machines. , 2018, Computer methods and programs in biomedicine.
[27] Cigdem Gunduz-Demir,et al. Unsupervised Feature Extraction via Deep Learning for Histopathological Classification of Colon Tissue Images , 2019, IEEE Transactions on Medical Imaging.
[28] S. P. Lloyd,et al. Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.
[29] Delbert Dueck,et al. Clustering by Passing Messages Between Data Points , 2007, Science.
[30] Qiang Ji,et al. A generative restricted Boltzmann machine based method for high-dimensional motion data modeling , 2015, Comput. Vis. Image Underst..
[31] Javier Hernando,et al. Restricted Boltzmann machines for vector representation of speech in speaker recognition , 2018, Comput. Speech Lang..
[32] Mohamad Ivan Fanany,et al. Kinematic features for human action recognition using Restricted Boltzmann Machines , 2016, 2016 4th International Conference on Information and Communication Technology (ICoICT).
[33] K. Verma,et al. Comparison of HMM- and SVM-based stroke classifiers for Gurmukhi script , 2017, Neural Computing and Applications.
[34] Feiping Nie,et al. Feature Selection via Global Redundancy Minimization , 2015, IEEE Transactions on Knowledge and Data Engineering.
[35] Larry S. Davis,et al. Learning structured ordinal measures for video based face recognition , 2018, Pattern Recognit..
[36] Jun Yang,et al. Improved traffic detection with support vector machine based on restricted Boltzmann machine , 2017, Soft Comput..
[37] Chee Peng Lim,et al. Classification of transcranial Doppler signals using individual and ensemble recurrent neural networks , 2017, Neurocomputing.
[38] Shuang Feng,et al. Generative and Discriminative Fuzzy Restricted Boltzmann Machine Learning for Text and Image Classification , 2020, IEEE Transactions on Cybernetics.
[39] Ming-Ai Li,et al. A novel feature extraction method for scene recognition based on Centered Convolutional Restricted Boltzmann Machines , 2015, Neurocomputing.
[40] Sean Hughes,et al. Clustering by Fast Search and Find of Density Peaks , 2016 .
[41] Satoshi Iso,et al. Scale-invariant Feature Extraction of Neural Network and Renormalization Group Flow , 2018, Physical review. E.
[42] Hong Wang,et al. Shared-nearest-neighbor-based clustering by fast search and find of density peaks , 2018, Inf. Sci..
[43] Xing Zhao,et al. Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[44] Qian Yu,et al. Rényi Divergence Based Generalization for Learning of Classification Restricted Boltzmann Machines , 2014, 2014 IEEE International Conference on Data Mining Workshop.
[45] Masato Okada,et al. Dynamical analysis of contrastive divergence learning: Restricted Boltzmann machines with Gaussian visible units , 2016, Neural Networks.
[46] Jiawei Han,et al. Document clustering using locality preserving indexing , 2005, IEEE Transactions on Knowledge and Data Engineering.
[47] Michael I. Jordan,et al. On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.
[48] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[49] Zhang Yi,et al. Graph Regularized Restricted Boltzmann Machine , 2018, IEEE Transactions on Neural Networks and Learning Systems.
[50] Geoffrey E. Hinton,et al. Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[51] Enrico Zio,et al. Fuzzy Classification With Restricted Boltzman Machines and Echo-State Networks for Predicting Potential Railway Door System Failures , 2015, IEEE Transactions on Reliability.
[52] C. L. Philip Chen,et al. Fuzzy Restricted Boltzmann Machine for the Enhancement of Deep Learning , 2015, IEEE Transactions on Fuzzy Systems.
[53] Menglong Yan,et al. Object recognition in remote sensing images using sparse deep belief networks , 2015 .
[54] Meng Wang,et al. MSRA-MM 2.0: A Large-Scale Web Multimedia Dataset , 2009, 2009 IEEE International Conference on Data Mining Workshops.
[55] Mario Fritz,et al. Advanced Steel Microstructural Classification by Deep Learning Methods , 2017, Scientific Reports.
[56] Tijmen Tieleman,et al. Training restricted Boltzmann machines using approximations to the likelihood gradient , 2008, ICML '08.
[57] Junwei Han,et al. Duplex Metric Learning for Image Set Classification , 2018, IEEE Transactions on Image Processing.
[58] G. Kane. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol 1: Foundations, vol 2: Psychological and Biological Models , 1994 .
[59] Miin-Shen Yang,et al. New similarity measures of intuitionistic fuzzy sets based on the Jaccard index with its application to clustering , 2018, Int. J. Intell. Syst..
[60] Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.
[61] Xiaofeng Zhu,et al. Local and Global Structure Preservation for Robust Unsupervised Spectral Feature Selection , 2018, IEEE Transactions on Knowledge and Data Engineering.
[62] Ronghua Shang,et al. Non-Negative Spectral Learning and Sparse Regression-Based Dual-Graph Regularized Feature Selection , 2018, IEEE Transactions on Cybernetics.
[63] Ali A. Alani,et al. Arabic Handwritten Digit Recognition Based on Restricted Boltzmann Machine and Convolutional Neural Networks , 2017, Inf..
[64] T. Metin Sezgin,et al. Sketch recognition with few examples , 2017, Comput. Graph..
[65] Yu-Gang Jiang,et al. Learning part-based mid-level representation for visual recognition , 2018, Neurocomputing.
[66] Shuyuan Yang,et al. Feature selection based dual-graph sparse non-negative matrix factorization for local discriminative clustering , 2018, Neurocomputing.
[67] Lei Wang,et al. 3D shape recognition and retrieval based on multi-modality deep learning , 2017, Neurocomputing.
[68] Geoffrey E. Hinton,et al. Application of Deep Belief Networks for Natural Language Understanding , 2014, IEEE/ACM Transactions on Audio, Speech, and Language Processing.
[69] Nikhil R. Pal,et al. Unsupervised Feature Selection with Controlled Redundancy (UFeSCoR) , 2015, IEEE Transactions on Knowledge and Data Engineering.
[70] Peng Jin,et al. Restricted Boltzmann Machines With Gaussian Visible Units Guided by Pairwise Constraints , 2019, IEEE Transactions on Cybernetics.
[71] Chris H. Q. Ding,et al. Orthogonal nonnegative matrix t-factorizations for clustering , 2006, KDD '06.
[72] Shukai Duan,et al. Enhancing electronic nose performance based on a novel QPSO-RBM technique , 2018 .
[73] Mohamed R. Amer,et al. Deep Multimodal Fusion: A Hybrid Approach , 2017, International Journal of Computer Vision.