Formal Convergence Analysis on Deterministic 1-Regularization based Mini-Batch Learning for RB

[1]  Eric W.T. Ngai,et al.  Deep learning in computer vision: A critical review of emerging techniques and application scenarios , 2021 .

[2]  Hui Ma,et al.  Open-Set Domain Adaptation in Machinery Fault Diagnostics Using Instance-Level Weighted Adversarial Learning , 2021, IEEE Transactions on Industrial Informatics.

[3]  Jiangjiang Wang,et al.  Incorporating deep learning of load predictions to enhance the optimal active energy management of combined cooling, heating and power system , 2021 .

[4]  Mohsen Guizani,et al.  Federated Learning Meets Human Emotions: A Decentralized Framework for Human–Computer Interaction for IoT Applications , 2021, IEEE Internet of Things Journal.

[5]  Nicolas Courty,et al.  Unbalanced minibatch Optimal Transport; applications to Domain Adaptation , 2021, ICML.

[6]  Wei Zhang,et al.  Federated learning for machinery fault diagnosis with dynamic validation and self-supervision , 2021, Knowl. Based Syst..

[7]  Jing Qiu,et al.  Deep reinforcement learning based home energy management system with devices operational dependencies , 2021, International Journal of Machine Learning and Cybernetics.

[8]  Huisheng Zhang,et al.  Deterministic convergence of complex mini-batch gradient learning algorithm for fully complex-valued neural networks , 2020, Neurocomputing.

[9]  Reza M. Parizi,et al.  Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications , 2020, IEEE Access.

[10]  J. Chen,et al.  Survey on Neural Network Architectures with Deep Learning , 2020, Journal of Soft Computing Paradigm.

[11]  Wendong Xiao,et al.  Non-iterative and Fast Deep Learning: Multilayer Extreme Learning Machines , 2020, J. Frankl. Inst..

[12]  Guang-Bin Huang,et al.  Deep and wide feature based extreme learning machine for image classification , 2020, Neurocomputing.

[13]  Zhong Jin,et al.  A discriminative deep association learning for facial expression recognition , 2019, International Journal of Machine Learning and Cybernetics.

[14]  Kamaledin Ghiasi-Shirazi,et al.  Improving the Backpropagation Algorithm with Consequentialism Weight Updates over Mini-Batches , 2020, Neurocomputing.

[15]  Dongliang Chang,et al.  GPCA: A Probabilistic Framework for Gaussian Process Embedded Channel Attention , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Farhad Pourpanah,et al.  Recent advances in deep learning , 2020, International Journal of Machine Learning and Cybernetics.

[17]  Zhongmin Wang,et al.  Emotion recognition using multimodal deep learning in multiple psychophysiological signals and video , 2020, Int. J. Mach. Learn. Cybern..

[18]  Andrew Chi-Sing Leung,et al.  Explicit Center Selection and Training for Fault Tolerant RBF Networks , 2019, ICONIP.

[19]  Hironobu Fujiyoshi,et al.  Deep learning-based image recognition for autonomous driving , 2019, IATSS Research.

[20]  Hing Cheung So,et al.  An $\ell_0$ -Norm-Based Centers Selection for Failure Tolerant RBF Networks , 2019, IEEE Access.

[21]  Davar Giveki,et al.  Designing a New Radial Basis Function Neural Network by Harmony Search for Diabetes Diagnosis , 2019, Optical Memory and Neural Networks.

[22]  Mita Nasipuri,et al.  SegFast-V2: Semantic image segmentation with less parameters in deep learning for autonomous driving , 2019, International Journal of Machine Learning and Cybernetics.

[23]  Chin-Teng Lin,et al.  Deep Sparse Representation Classifier for facial recognition and detection system , 2019, Pattern Recognit. Lett..

[24]  Chika Yinka-Banjo,et al.  A review of generative adversarial networks and its application in cybersecurity , 2019, Artificial Intelligence Review.

[25]  Mariette Awad,et al.  On extreme learning machines in sequential and time series prediction: A non-iterative and approximate training algorithm for recurrent neural networks , 2019, Neurocomputing.

[26]  P. J. García-Nieto,et al.  Review: machine learning techniques applied to cybersecurity , 2019, International Journal of Machine Learning and Cybernetics.

[27]  Jie Zhang,et al.  Residual compensation extreme learning machine for regression , 2018, Neurocomputing.

[28]  Chi-Sing Leung,et al.  ADMM-Based Algorithm for Training Fault Tolerant RBF Networks and Selecting Centers , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[29]  Davar Giveki,et al.  A New Neural Network Classifier Based on Atanassov’s Intuitionistic Fuzzy Set Theory , 2018, Optical Memory and Neural Networks.

[30]  Axel Munk,et al.  Optimal Transport: Fast Probabilistic Approximation with Exact Solvers , 2018, J. Mach. Learn. Res..

[31]  Jianping Yin,et al.  Distributed and asynchronous Stochastic Gradient Descent with variance reduction , 2017, Neurocomputing.

[32]  Weidong Yang,et al.  Class-specific cost regulation extreme learning machine for imbalanced classification , 2017, Neurocomputing.

[33]  Anthony G. Constantinides,et al.  Lagrange Programming Neural Network for Nondifferentiable Optimization Problems in Sparse Approximation , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[34]  Tie-Yan Liu,et al.  Convergence Analysis of Distributed Stochastic Gradient Descent with Shuffling , 2017, Neurocomputing.

[35]  Andrew Chi-Sing Leung,et al.  A Regularizer Approach for RBF Networks Under the Concurrent Weight Failure Situation , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[36]  Amit Jain,et al.  Analysis & survey on fault tolerance in radial basis function networks , 2015, International Conference on Computing, Communication & Automation.

[37]  Andrew Chi-Sing Leung,et al.  Online training and its convergence for faulty networks with multiplicative weight noise , 2015, Neurocomputing.

[38]  Guang-Bin Huang,et al.  Trends in extreme learning machines: A review , 2015, Neural Networks.

[39]  Alice C. Parker,et al.  Synaptic Variability in a Cortical Neuromorphic Circuit , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[40]  Caro Lucas,et al.  Relaxed Fault-Tolerant Hardware Implementation of Neural Networks in the Presence of Multiple Transient Errors , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[41]  Andrew Chi-Sing Leung,et al.  RBF Networks Under the Concurrent Fault Situation , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[42]  David Zhang,et al.  Fisher Discrimination Dictionary Learning for sparse representation , 2011, 2011 International Conference on Computer Vision.

[43]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[44]  Peng Shi,et al.  Convergence analysis of sparse LMS algorithms with l1-norm penalty based on white input signal , 2010, Signal Process..

[45]  Andrew Chi-Sing Leung,et al.  On the Selection of Weight Decay Parameter for Faulty Networks , 2010, IEEE Transactions on Neural Networks.

[46]  John Langford,et al.  Sparse Online Learning via Truncated Gradient , 2008, NIPS.

[47]  Thomas S. Huang,et al.  Image super-resolution as sparse representation of raw image patches , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[48]  Andrew Chi-Sing Leung,et al.  A Fault-Tolerant Regularizer for RBF Networks , 2008, IEEE Transactions on Neural Networks.

[49]  Dingli Yu,et al.  Selecting radial basis function network centers with recursive orthogonal least squares training , 2000, IEEE Trans. Neural Networks Learn. Syst..

[50]  Ignacio Rojas,et al.  An Accurate Measure for Multilayer Perceptron Tolerance to Weight Deviations , 1999, Neural Processing Letters.

[51]  C. L. Philip Chen,et al.  A rapid learning and dynamic stepwise updating algorithm for flat neural networks and the application to time-series prediction , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[52]  Dan Simon,et al.  Fault-tolerant training for optimal interpolative nets , 1995, IEEE Trans. Neural Networks.

[53]  Sheng Chen Nonlinear time series modelling and prediction using Gaussian RBF networks with enhanced clustering and RLS learning , 1995 .

[54]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[55]  F. Girosi,et al.  Networks for approximation and learning , 1990, Proc. IEEE.

[56]  B. Liu,et al.  Error analysis of digital filters realized with floating-point arithmetic , 1969 .

[57]  Alex Alexandridis,et al.  Wind turbine power curve modeling using radial basis function neural networks and tabu search , 2021 .

[58]  Andrew Chi-Sing Leung,et al.  Convergence of Mini-Batch Learning for Fault Aware RBF Networks , 2020, ICONIP.

[59]  C. L. Philip Chen,et al.  Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[60]  Jesús Alcalá-Fdez,et al.  KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework , 2011, J. Multiple Valued Log. Soft Comput..

[61]  Sheng Chen,et al.  Local regularization assisted orthogonal least squares regression , 2006, Neurocomputing.

[62]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[63]  Jim Austin,et al.  Fault Tolerant Multi-Layer Perceptron Networks , 1992 .