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Perceptron: Learning, Generalization, Model Selection, Fault Tolerance, and Role in the Deep Learning Era

https://doi.org/10.3390/math10244730
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Nonlinear principal component analysis using autoassociative neural networks
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Neural Networks and Statistical Learning
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Self-Organization and Associative Memory
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BFGS Optimization for Faster and Automated Supervised Learning
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Accelerating the convergence of the back-propagation method
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Auto-association by multilayer perceptrons and singular value decomposition
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A logical calculus of the ideas immanent in nervous activity
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Approximation by superpositions of a sigmoidal function
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The Levenberg-Marquardt algorithm: Implementation and theory
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Evolving Fault-Tolerant Neural Networks
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Tracking the best hyperplane with a simple budget Perceptron
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Gradient descent optimizes over-parameterized deep ReLU networks
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Parameter by Parameter Algorithm for Multilayer Perceptrons
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Constructive Approximation of Discontinuous Functions by Neural Networks
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Fast and Stable Learning Utilizing Singular Regions of Multilayer Perceptron
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Connectionist learning procedures
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Learning of modular structured networks
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Increased rates of convergence through learning rate adaptation
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On the approximate realization of continuous mappings by neural networks
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Multilayer feedforward networks are universal approximators
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SuperSAB: Fast adaptive back propagation with good scaling properties
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Linear programming algorithm for neural networks
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Quadratic function nodes: Use, structure and training
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Rescaling of variables in back propagation learning
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Back-propagation algorithm which varies the number of hidden units
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Creating artificial neural networks that generalize
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Initializing back propagation networks with prototypes
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Speed up learning and network optimization with extended back propagation
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Minimisation methods for training feedforward neural networks
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Information geometry of the EM and em algorithms for neural networks
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Complex associative memory
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Selective training of feedforward artificial neural networks using matrix perturbation theory
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Learning with first, second, and no derivatives: A case study in high energy physics
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Variable selection with neural networks
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ADAPTIVE SIGNAL PROCESSING FOR ADAPTIVE CONTROL
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A dynamic MLP-based DDoS attack detection method using feature selection and feedback
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Mini-max initialization for function approximation
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A modified error function for the backpropagation algorithm
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An activation function adapting training algorithm for sigmoidal feedforward networks
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Local regularization assisted orthogonal least squares regression
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Adaptive conjugate gradient algorithm for perceptron training
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An efficient hidden layer training method for the multilayer perceptron
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Natural conjugate gradient training of multilayer perceptrons
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Perceptron training algorithms designed using discrete-time control Liapunov functions
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Self-scaled conjugate gradient training algorithms
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A learning rule for very simple universal approximators consisting of a single layer of perceptrons
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Learning in compressed space
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Error bounds for approximations with deep ReLU networks
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The capacity of feedforward neural networks
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Biologically plausible deep learning — But how far can we go with shallow networks?
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Transformed ℓ1 regularization for learning sparse deep neural networks
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An analysis of training and generalization errors in shallow and deep networks
inferredcites →https://doi.org/10.1016/j.neunet.2019.09.016openalex-snapshot
Noise-boosted bidirectional backpropagation and adversarial learning
inferredcites →https://doi.org/10.1016/j.neunet.2019.11.006openalex-snapshot
Dimension independent bounds for general shallow networks
inferredcites →https://doi.org/10.1016/j.neunet.2020.01.011openalex-snapshot
Adaptive complex-valued stepsize based fast learning of complex-valued neural networks
inferredcites →https://doi.org/10.1016/j.neunet.2020.02.012openalex-snapshot
Parametric Deformable Exponential Linear Units for deep neural networks
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Noise can speed backpropagation learning and deep bidirectional pretraining
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Theory of adaptive SVD regularization for deep neural networks
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Efficient architecture for deep neural networks with heterogeneous sensitivity
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An effective SteinGLM initialization scheme for training multi-layer feedforward sigmoidal neural networks
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Deep ReLU neural networks in high-dimensional approximation
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Non-differentiable saddle points and sub-optimal local minima exist for deep ReLU networks
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On the approximation of functions by tanh neural networks
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Understanding and mitigating noise in trained deep neural networks
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Feedforward neural networks initialization based on discriminant learning
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LOss-Based SensiTivity rEgulaRization: Towards deep sparse neural networks
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Brain-inspired chaotic backpropagation for MLP
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A new convex objective function for the supervised learning of single-layer neural networks
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Training effective deep reinforcement learning agents for real-time life-cycle production optimization
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A fuzzy rule based backpropagation method for training binary multilayer perceptrons
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A fuzzy backpropagation algorithm
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A fuzzy rule-based algorithm to train perceptrons
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Practical complexity control in multilayer perceptrons
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Deterministic convergence of an online gradient method for neural networks
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Efficient perceptron learning using constrained steepest descent
inferredcites →https://doi.org/10.1016/s0893-6080(00)00085-xopenalex-snapshot
Quick fuzzy backpropagation algorithm
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A pruning method for the recursive least squared algorithm
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Optimal design of regularization term and regularization parameter by subspace information criterion
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Dual extended Kalman filtering in recurrent neural networks
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The general inefficiency of batch training for gradient descent learning
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Steepest descent with momentum for quadratic functions is a version of the conjugate gradient method
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On a class of efficient learning algorithms for neural networks
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A scaled conjugate gradient algorithm for fast supervised learning
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An Efficient EM-based Training Algorithm for Feedforward Neural Networks
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Effective Backpropagation Training with Variable Stepsize
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Efficient Backpropagation Learning Using Optimal Learning Rate and Momentum
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Premature Saturation in Backpropagation Networks: Mechanism and Necessary Conditions
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Automatic early stopping using cross validation: quantifying the criteria
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Weight decay backpropagation for noisy data
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A modified back-propagation method to avoid false local minima
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A second-order learning algorithm for multilayer networks based on block Hessian matrix
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A novel learning algorithm which improves the partial fault tolerance of multilayer neural networks
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Modelling with constructive backpropagation
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Accelerating neural network training using weight extrapolations
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Pruning of basis functions in nonlinear approximators
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Learning efficiency improvement of back-propagation algorithm by error saturation prevention method
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Successive approximation training algorithm for feedforward neural networks
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An independent component analysis based weight initialization method for multilayer perceptrons
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A three-term backpropagation algorithm
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Performance improvement of backpropagation algorithm by automatic activation function gain tuning using fuzzy logic
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A new method in determining initial weights of feedforward neural networks for training enhancement
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Training a feed-forward network by feeding gradients forward rather than by back-propagation of errors
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MLP iterative construction algorithm
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A recursive algorithm based on the extended Kalman filter for the training of feedforward neural models
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A neural network training algorithm utilizing multiple sets of linear equations
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A weight initialization method for improving training speed in feedforward neural network
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Improving the tolerance of multilayer perceptrons by minimizing the statistical sensitivity to weight deviations
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Large Margin Classification Using the Perceptron Algorithm
inferredcites →https://doi.org/10.1023/a:1009698206772openalex-snapshot
Obtaining Fault Tolerant Multilayer Perceptrons Using an Explicit Regularization
inferredcites →https://doi.org/10.1023/a:1018671912219openalex-snapshot
Initialization of Supervised Training for Parametric Estimation
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A Variable Memory Quasi-Newton Training Algorithm
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An Accurate Measure for Multilayer Perceptron Tolerance to Weight Deviations
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Generalization bounds for function approximation from scattered noisy data
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Assessing the Noise Immunity and Generalization of Radial Basis Function Networks
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The perceptron: A probabilistic model for information storage and organization in the brain.
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A comparison of fast training algorithms over two real problems
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New approach to selection of initial values of weights in neural function approximation
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Extended backpropagation algorithm
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Attractor-based trust-region algorithm for efficient training of multilayer perceptrons
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Conjugate gradient algorithm for efficient training of artificial neural networks
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Model structure selection for multivariable systems by cross-validation methods
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Quadratic optimization method for multilayer neural networks with local error-backpropagation
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Fast training of neural networks for remote sensing
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Analysis and optimization of neural networks for remote sensing
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Using Relevance to Reduce Network Size Automatically
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Learning algorithms with optimal stability in neural networks
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Learning in feedforward layered networks: the tiling algorithm
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Nonlinear Hebbian training of the perceptron
inferredcites →https://doi.org/10.1093/comjnl/42.4.260openalex-snapshot
Hypothesis Selection and Testing by the MDL Principle
inferredcites →https://doi.org/10.1093/comjnl/7.2.149openalex-snapshot
Function minimization by conjugate gradients
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Conjugate Gradient Algorithms: Quadratic Termination without Linear Searches
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Neural Networks for Pattern Recognition
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Finite precision error analysis of neural network hardware implementations
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Universal approximation bounds for superpositions of a sigmoidal function
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Functional-link net computing: theory, system architecture, and functionalities
inferredcites →https://doi.org/10.1109/21.101159openalex-snapshot
Benefits of gain: speeded learning and minimal hidden layers in back-propagation networks
inferredcites →https://doi.org/10.1109/34.141559openalex-snapshot
Comparative analysis of backpropagation and the extended Kalman filter for training multilayer perceptrons
inferredcites →https://doi.org/10.1109/5.58323openalex-snapshot
30 years of adaptive neural networks: perceptron, Madaline, and backpropagation
inferredcites →https://doi.org/10.1109/5.58326openalex-snapshot
Networks for approximation and learning
inferredcites →https://doi.org/10.1109/5.58337openalex-snapshot
Backpropagation through time: what it does and how to do it
inferredcites →https://doi.org/10.1109/5.726791openalex-snapshot
Gradient-based learning applied to document recognition
inferredcites →https://doi.org/10.1109/72.105414openalex-snapshot
Maximally fault tolerant neural networks
inferredcites →https://doi.org/10.1109/72.105417openalex-snapshot
Adaptive Ho-Kashyap rules for perceptron training
inferredcites →https://doi.org/10.1109/72.105422openalex-snapshot
Sensitivity analysis of multilayer perceptron with differentiable activation functions
inferredcites →https://doi.org/10.1109/72.125864openalex-snapshot
Optimization for training neural nets
inferredcites →https://doi.org/10.1109/72.125875openalex-snapshot
Designing multilayer perceptrons from nearest-neighbor systems
inferredcites →https://doi.org/10.1109/72.143378openalex-snapshot
Statistically controlled activation weight initialization (SCAWI)
inferredcites →https://doi.org/10.1109/72.165592openalex-snapshot
Avoiding false local minima by proper initialization of connections
inferredcites →https://doi.org/10.1109/72.165600openalex-snapshot
Improving generalization performance using double backpropagation
inferredcites →https://doi.org/10.1109/72.248452openalex-snapshot
Pruning algorithms-a survey
inferredcites →https://doi.org/10.1109/72.248456openalex-snapshot
Determining and improving the fault tolerance of multilayer perceptrons in a pattern-recognition application
inferredcites →https://doi.org/10.1109/72.286921openalex-snapshot
An accelerated learning algorithm for multilayer perceptron networks
inferredcites →https://doi.org/10.1109/72.317730openalex-snapshot
Enhanced MLP performance and fault tolerance resulting from synaptic weight noise during training
inferredcites →https://doi.org/10.1109/72.329683openalex-snapshot
Network information criterion-determining the number of hidden units for an artificial neural network model
inferredcites →https://doi.org/10.1109/72.329690openalex-snapshot
Connectivity and performance tradeoffs in the cascade correlation learning architecture
inferredcites →https://doi.org/10.1109/72.329697openalex-snapshot
Training feedforward networks with the Marquardt algorithm
inferredcites →https://doi.org/10.1109/72.363426openalex-snapshot
Use of a quasi-Newton method in a feedforward neural network construction algorithm
inferredcites →https://doi.org/10.1109/72.363452openalex-snapshot
An accelerated learning algorithm for multilayer perceptrons: optimization layer by layer
inferredcites →https://doi.org/10.1109/72.363478openalex-snapshot
The selection of weight accuracies for Madalines
inferredcites →https://doi.org/10.1109/72.363479openalex-snapshot
Complete and partial fault tolerance of feedforward neural nets
inferredcites →https://doi.org/10.1109/72.377960openalex-snapshot
Similarities of error regularization, sigmoid gain scaling, target smoothing, and training with jitter
inferredcites →https://doi.org/10.1109/72.377972openalex-snapshot
Dynamic learning rate optimization of the backpropagation algorithm
inferredcites →https://doi.org/10.1109/72.410351openalex-snapshot
On the application of orthogonal transformation for the design and analysis of feedforward networks
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Fault-tolerant training for optimal interpolative nets
inferredcites →https://doi.org/10.1109/72.478388openalex-snapshot
The dependence identification neural network construction algorithm
inferredcites →https://doi.org/10.1109/72.478415openalex-snapshot
Optimal convergence of on-line backpropagation
inferredcites →https://doi.org/10.1109/72.548172openalex-snapshot
A generalized learning paradigm exploiting the structure of feedforward neural networks
inferredcites →https://doi.org/10.1109/72.557662openalex-snapshot
Capabilities of a four-layered feedforward neural network: four layers versus three
inferredcites →https://doi.org/10.1109/72.557673openalex-snapshot
High-order and multilayer perceptron initialization
inferredcites →https://doi.org/10.1109/72.572092openalex-snapshot
An iterative pruning algorithm for feedforward neural networks
inferredcites →https://doi.org/10.1109/72.572101openalex-snapshot
On convergence properties of pocket algorithm
inferredcites →https://doi.org/10.1109/72.572103openalex-snapshot
Fast parallel off-line training of multilayer perceptrons
inferredcites →https://doi.org/10.1109/72.572117openalex-snapshot
Improving the error backpropagation algorithm with a modified error function
inferredcites →https://doi.org/10.1109/72.623200openalex-snapshot
Asymptotic statistical theory of overtraining and cross-validation
inferredcites →https://doi.org/10.1109/72.623214openalex-snapshot
Objective functions for training new hidden units in constructive neural networks
inferredcites →https://doi.org/10.1109/72.701180openalex-snapshot
A hybrid linear/nonlinear training algorithm for feedforward neural networks
inferredcites →https://doi.org/10.1109/72.737492openalex-snapshot
Dynamic tunneling technique for efficient training of multilayer perceptrons
inferredcites →https://doi.org/10.1109/72.737502openalex-snapshot
On the Kalman filtering method in neural network training and pruning
inferredcites →https://doi.org/10.1109/72.774266openalex-snapshot
A fast U-D factorization-based learning algorithm with applications to nonlinear system modeling and identification
inferredcites →https://doi.org/10.1109/72.774273openalex-snapshot
A formal selection and pruning algorithm for feedforward artificial neural network optimization
inferredcites →https://doi.org/10.1109/72.788648openalex-snapshot
Model complexity control for regression using VC generalization bounds
inferredcites →https://doi.org/10.1109/72.80206openalex-snapshot
Sensitivity of feedforward neural networks to weight errors
inferredcites →https://doi.org/10.1109/72.80230openalex-snapshot
Perceptron-based learning algorithms
inferredcites →https://doi.org/10.1109/72.80236openalex-snapshot
A simple procedure for pruning back-propagation trained neural networks
inferredcites →https://doi.org/10.1109/72.809096openalex-snapshot
The "weight smoothing" regularization of MLP for Jacobian stabilization
inferredcites →https://doi.org/10.1109/72.822509openalex-snapshot
LMS learning algorithms: misconceptions and new results on converence
inferredcites →https://doi.org/10.1109/72.839001openalex-snapshot
The layer-wise method and the backpropagation hybrid approach to learning a feedforward neural network
inferredcites →https://doi.org/10.1109/72.839017openalex-snapshot
A local linearized least squares algorithm for training feedforward neural networks
inferredcites →https://doi.org/10.1109/72.914538openalex-snapshot
Feedforward networks training speed enhancement by optimal initialization of the synaptic coefficients
inferredcites →https://doi.org/10.1109/72.963763openalex-snapshot
H/sub ∞/-learning of layered neural networks
inferredcites →https://doi.org/10.1109/72.963768openalex-snapshot
Two regularizers for recursive least squared algorithms in feedforward multilayered neural networks
inferredcites →https://doi.org/10.1109/72.963771openalex-snapshot
Distributed fault tolerance in optimal interpolative nets
inferredcites →https://doi.org/10.1109/72.963775openalex-snapshot
A new pruning heuristic based on variance analysis of sensitivity information
inferredcites →https://doi.org/10.1109/72.977323openalex-snapshot
A general backpropagation algorithm for feedforward neural networks learning
inferredcites →https://doi.org/10.1109/78.124956openalex-snapshot
Fast learning process of multilayer neural networks using recursive least squares method
inferredcites →https://doi.org/10.1109/78.127966openalex-snapshot
A real-time learning algorithm for a multilayered neural network based on the extended Kalman filter
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