Related references
Note: Only part of the references are listed.Convergence and objective functions of noise-injected multilayer perceptrons with hidden multipliers
Xiangyu Wang et al.
NEUROCOMPUTING (2021)
Feature Selection Using a Neural Network With Group Lasso Regularization and Controlled Redundancy
Jian Wang et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)
Deterministic convergence analysis via smoothing group Lasso regularization and adaptive momentum for Sigma-Pi-Sigma neural network
Qian Kang et al.
INFORMATION SCIENCES (2021)
Convergence of a Gradient-Based Learning Algorithm With Penalty for Ridge Polynomial Neural Networks
Qinwei Fan et al.
IEEE ACCESS (2021)
Learning Optimized Structure of Neural Networks by Hidden Node Pruning With L1 Regularization
Xuetao Xie et al.
IEEE TRANSACTIONS ON CYBERNETICS (2020)
Feature Selection for Neural Networks Using Group Lasso Regularization
Huaqing Zhang et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2020)
Deterministic convergence of complex mini-batch gradient learning algorithm for fully complex-valued neural networks
Huisheng Zhang et al.
NEUROCOMPUTING (2020)
Weight Noise Injection-Based MLPs With Group Lasso Penalty: Asymptotic Convergence and Application to Node Pruning
Jian Wang et al.
IEEE TRANSACTIONS ON CYBERNETICS (2019)
Group L1/2 Regularization for Pruning Hidden Layer Nodes of Feedforward Neural Networks
Habtamu Zegeye Alemu et al.
IEEE ACCESS (2019)
L0 Regularization based Fine-grained Neural Network Pruning Method
Qixin Xie et al.
PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON ELECTRONICS, COMPUTERS AND ARTIFICIAL INTELLIGENCE (ECAI-2019) (2019)
L1/2 regularization learning for smoothing interval neural networks: Algorithms and convergence analysis
Dakun Yang et al.
NEUROCOMPUTING (2018)
Smooth group L-1/2 regularization for input layer of feedforward neural networks
Feng Li et al.
NEUROCOMPUTING (2018)
A Novel Pruning Algorithm for Smoothing Feedforward Neural Networks Based on Group Lasso Method
Jian Wang et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2018)
Convergence analyses on sparse feedforward neural networks via group lasso regularization
Jian Wang et al.
INFORMATION SCIENCES (2017)
Group sparse regularization for deep neural networks
Simone Scardapane et al.
NEUROCOMPUTING (2017)
Online gradient method with smoothing l0 regularization for feedforward neural networks
Huisheng Zhang et al.
NEUROCOMPUTING (2017)
Input Layer Regularization of Multilayer Feedforward Neural Networks
Feng Li et al.
IEEE ACCESS (2017)
Alternating Iteration for I-p (0 < p <= 1) Regularized CT Reconstruction
Chuang Miao et al.
IEEE ACCESS (2016)
Batch gradient training method with smoothing regularization for l0 feedforward neural networks
Huisheng Zhang et al.
NEURAL COMPUTING & APPLICATIONS (2015)
Batch gradient method with smoothing L1/2 regularization for training of feedforward neural networks
Wei Wu et al.
NEURAL NETWORKS (2014)
Boundedness and convergence of batch back-propagation algorithm with penalty for feedforward neural networks
Huisheng Zhang et al.
NEUROCOMPUTING (2012)
L1/2 Regularization: A Thresholding Representation Theory and a Fast Solver
Zongben Xu et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2012)
On a Variational Norm Tailored to Variable-Basis Approximation Schemes
Giorgio Gnecco et al.
IEEE TRANSACTIONS ON INFORMATION THEORY (2011)
Convergence analysis of online gradient method for BP neural networks
Wei Wu et al.
NEURAL NETWORKS (2011)
A Novel Pruning Algorithm for Optimizing Feedforward Neural Network of Classification Problems
M. Gethsiyal Augasta et al.
NEURAL PROCESSING LETTERS (2011)
Deterministic convergence of conjugate gradient method for feedforward neural networks
Jian Wang et al.
NEUROCOMPUTING (2011)
L (1/2) regularization
Xu ZongBen et al.
SCIENCE CHINA-INFORMATION SCIENCES (2010)
Hidden neuron pruning of multilayer perceptrons using a quantified sensitivity measure
XQ Zeng et al.
NEUROCOMPUTING (2006)
Bounds on rates of variable-basis and neural-network approximation
V Kurková et al.
IEEE TRANSACTIONS ON INFORMATION THEORY (2001)
Share Paper
