Similarities of error regularization, sigmoid gain scaling, target smoothing, and training with jitter

No abstract is available for this paper.

• Publication year

  1995

• Venue

  IEEE Trans. Neural Networks

• Publication date

  1995-05-01

• Fields of study

  Mathematics, Computer Science, Medicine

• Identifiers
  DOI 10.1109/72.377960PMID 18263340
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

• Functional approximation by feed-forward networks: a least-squares approach to generalization

  1994cited by this paper
• Pruning algorithms-a survey

  1993cited by this paper
• Curvature-driven smoothing: a learning algorithm for feedforward networks

  1993cited by this paper
• Methods for choosing the regularization parameter and estimating the noise variance in image restoration and their relation

  1992cited by this paper
• Fault tolerance of the backpropagation neural network trained on noisy inputs

  1992cited by this paper
• Using additive noise in back-propagation training

  1992cited by this paper
• Noise injection into inputs in back-propagation learning

  1992cited by this paper
• An equivalence between sigmoidal gain scaling and training with noisy (jittered) input data

  1992cited by this paper
• Regularization using jittered training data

  1992cited by this paper
• Curvature-Driven Smoothing in Backpropagation Neural Networks

  1992cited by this paper
• Learning with limited numerical precision using the cascade-correlation algorithm

  1992cited by this paper
• Creating artificial neural networks that generalize

  1991cited by this paper
• A Simple Weight Decay Can Improve Generalization

  1991cited by this paper
• Kernel regression and backpropagation training with noise

  1991cited by this paper
• Improving the Generalization Properties of Radial Basis Function Neural Networks

  1991cited by this paper
• Generalization by weight-elimination applied to currency exchange rate prediction

  1991cited by this paper
• Query based learning in a multilayered perceptron in the presence of data jitter

  1991cited by this paper
• Double backpropagation increasing generalization performance

  1991cited by this paper
• Generalization by Weight-Elimination with Application to Forecasting

  1990cited by this paper
• Networks for approximation and learning

  1990cited by this paper
• Statistical dynamics of learning

  1990cited by this paper
• An Approach to Generalization Problem in Back-Propagation Learning

  1990cited by this paper
• An Improved Version of the Pseudo-Inverse Solution for Classification and Neural Networks

  1989cited by this paper
• Inversion of multilayer nets

  1989cited by this paper
• Fast Learning in Networks of Locally-Tuned Processing Units

  1989cited by this paper
• Neural net pruning-why and how

  1988cited by this paper
• Learning the hidden structure of speech.

  1988cited by this paper
• Experiments on Learning by Back Propagation.

  1986cited by this paper

• Fault diagnosis of electromechanical systems under coupled fault characteristics based on digital twin-integrated multi-task graph neural network

  2026cites this paper
• Assessing Interference with Regression Analysis Techniques

  2024cites this paper
• Optimized injection of noise in activation functions to improve generalization of neural networks

  2024cites this paper
• Learning Provably Robust Estimators for Inverse Problems via Jittering

  2023cites this paper
• Noise-boosted recurrent backpropagation

  2023cites this paper
• Perceptron: Learning, Generalization, Model Selection, Fault Tolerance, and Role in the Deep Learning Era

  2022cites this paper
• Artificial intelligence based ensemble modeling of wastewater treatment plant using jittered data

  2021cites this paper
• Regularization Effect of Random Node Fault/Noise on Gradient Descent Learning Algorithm

  2021influential citation
• Noise-Boosted Backpropagation Learning of Feedforward Threshold Neural Networks for Function Approximation

  2021cites this paper
• Granger causality detection in high-dimensional systems using feedforward neural networks

  2020cites this paper
• A Limitation of Gradient Descent Learning

  2020cites this paper
• Noise can speed backpropagation learning and deep bidirectional pretraining

  2020cites this paper
• Relieving the Need for Bi-Level Decision-Making for Optimal Retail Pricing via Online Meta-Prediction of Data-Driven Demand Response of HVAC Systems

  2020cites this paper
• Robust Neural Networks Learning via a Minimization of Stochastic Output Sensitivity

  2020cites this paper
• A Binary-Activation, Multi-Level Weight RNN and Training Algorithm for ADC-/DAC-Free and Noise-Resilient Processing-in-Memory Inference With eNVM

  2020cites this paper
• A noise injection strategy for graph autoencoder training

  2020cites this paper
• Incorporating Image Gradients as Secondary Input Associated with Input Image to Improve the Performance of the CNN Model

  2020cites this paper
• Retail electricity pricing via online-learning of data-driven demand response of HVAC systems

  2020cites this paper
• Data pre-post processing methods in AI-based modeling of seepage through earthen dams

  2019cites this paper
• A binary-activation, multi-level weight RNN and training algorithm for processing-in-memory inference with eNVM

  2019cites this paper
• Waste generation prediction under uncertainty in smart cities through deep neuroevolution

  2019cites this paper
• Convolutional Neural Networks for Computer Aided Diagnosis of Interdental and Rustling Sigmatism

  2019cites this paper
• A Supervised-Learning-Based Strategy for Optimal Demand Response of an HVAC System in a Multi-Zone Office Building

  2019cites this paper
• Fundamentals of Machine Learning

  2019cites this paper
• Application of artificial neural networks to X‐ray fluorescence spectrum analysis

  2019cites this paper
• Learning Algorithm for Boltzmann Machines With Additive Weight and Bias Noise

  2019cites this paper
• Hybrid denoising-jittering data pre-processing approach to enhance multi-step-ahead rainfall–runoff modeling

  2018cites this paper
• Evolutionary Deep Learning for Car Park Occupancy Prediction in Smart Cities

  2018cites this paper
• Waste Generation Prediction in Smart Cities Through Deep Neuroevolution

  2018cites this paper
• Stochasticity-Assisted Training in Artificial Neural Network

  2018cites this paper
• Improving the resilience of neural network solution of inverse problems in Raman spectroscopy of multi-component solutions of inorganic compounds to the distortions caused by frequency shift of the spectral channels

  2018cites this paper
• Fault and Error Tolerance in Neural Networks: A Review

  2017cites this paper
• Multi-style learning with denoising autoencoders for acoustic modeling in the internet of things (IoT)

  2017cites this paper
• Metaheuristic design of feedforward neural networks: A review of two decades of research

  2017cites this paper
• Técnicas de Regularização de Modelos Neurais Aplicadas à Previsão de Carga a Curto Prazo

  2016cites this paper
• Hybrid denoising-jittering data processing approach to enhance sediment load prediction of muddy rivers

  2016cites this paper
• Correcting Writing Errors with Convolutional Neural Networks

  2016cites this paper
• Modelos Neurais Autônomos Para Previsão De Carga Elétrica

  2016cites this paper
• Feature Selection and Function Approximation Using Adaptive Algorithms

  2016cites this paper
• Noise on Gradient Systems with Forgetting

  2015cites this paper
• Noisy training for deep neural networks in speech recognition

  2015cites this paper
• Classification of Power Signal by using S-Transform and PSO based FLANN

  2014cites this paper
• Noisy training for deep neural networks

  2014cites this paper
• Effect of Noise on Gradient Systems

  2013cites this paper
• Evolutionary Inversion of Swarm Emergence Using Disjunctive Combs Control

  2013cites this paper
• A comparison of methods to avoid overfitting in neural networks training in the case of catchment runoff modelling

  2013cites this paper
• Product-Units neural networks for catchment runoff forecasting

  2012cites this paper
• Comparison of evolutionary computation techniques for noise injected neural network training to estimate longitudinal dispersion coefficients in rivers

  2012cites this paper
• Learning with Querying and its Application in Network Security

  2012cites this paper
• PRODUCT UNIT NEURAL NETWORKS FOR ESTIMATIONS OF LONGITUDINAL DISPERSION COEFFICIENTS IN RIVERS

  2012cites this paper
• B lancos Blandos Enfatizados para Clasificacion Maquina

  2011cites this paper
• Optimizing neural networks for river flow forecasting – Evolutionary Computation methods versus the Levenberg–Marquardt approach

  2011cites this paper
• Convergence and Objective Functions of Some Fault/Noise-Injection-Based Online Learning Algorithms for RBF Networks

  2010cites this paper
• Finding Birds in Trees: Building Categories from Image Streams

  2010cites this paper
• Uncertainty study of data-based models of pollutant transport in rivers

  2010cites this paper
• Brief survey on online injecting fault / noise-based fault tolerant learning algorithms

  2009cites this paper
• On Node-Fault-Injection Training of an RBF Network

  2009cites this paper
• Improving performance of neural classifiers via selective reduction of target levels

  2009cites this paper
• Designing Model Based Classifiers by Emphasizing Soft Targets

  2009cites this paper
• On Weight-Noise-Injection Training

  2009cites this paper
• Fault Tolerant Learning for Neural Networks: Survey, Framework and Future Work

  2009cites this paper
• A Generic Neural Network Framework Using Design Patterns

  2008cites this paper
• Continuous spatially distributed simulation of surface and subsurface hydrological processes in a small semiarid catchment

  2008cites this paper
• Applying emphasized soft targets for Gaussian Mixture Model based classification

  2008cites this paper
• An emphasized target smoothing procedure to improve MLP classifiers performance

  2008cites this paper
• Edinburgh Research Explorer Regularization and Complexity Control in Feed-forward Networks

  2008cites this paper
• A neural network ensemble method with jittered training data for time series forecasting

  2007cites this paper
• Toward Estimating Autonomous Neural Network-Based Electric Load Forecasters

  2007cites this paper
• Constructive neural network ensemble for regression tasks in high dimensional spaces

  2007cites this paper
• Polynomial and RBF Kernels as Marker Selection Tools-A Breast Cancer Case Study

  2007cites this paper
• Computational Intelligence: Concepts to Implementations

  2007cites this paper
• Very Fast Online Learning of Highly Non Linear Problems

  2007cites this paper
• Noise-injected neural networks show promise for use on small-sample expression data

  2006cites this paper
• Approaches to Adaptive Stochastic Search Based on the Nonextensive Q-Distribution

  2006cites this paper
• Disease Diagnosis Using Query-Based Neural Networks

  2005cites this paper
• Mining Data by Query-Based Error-Propagation

  2005cites this paper
• Complexity Control of Neural Models for Load Forecasting

  2005cites this paper
• Regularization of short term load forecasting neural models

  2005cites this paper
• Support Vector Machines : principes d'induction, Réglage automatique et connaissances à priori

  2004cites this paper
• Regularizing the effect of input noise injection in feedforward neural networks training

  2004cites this paper
• Query-based learning for aerospace applications

  2003cites this paper
• Regularization parameter estimation for feedforward neural networks

  2003cites this paper
• Use of Noise to Augment Training Data: A Neural Network Method of Mineral–Potential Mapping in Regions of Limited Known Deposit Examples

  2003cites this paper
• Observational Learning Algorithm for an Ensemble of Neural Networks

  2002cites this paper
• On learning feedforward neural networks with noise injection into inputs

  2002cites this paper
• Implicit learning in autoencoder novelty assessment

  2002cites this paper
• Studies of model selection and regularization for generalization in neural networks with applications

  2002cites this paper
• On Different Facets of Regularization Theory

  2002cites this paper
• Computational explorations of the influence of structured knowledge on age-related cognitive decline.

  2002cites this paper
• Stabilizing weak classifiers: regularization and combining techniques in discriminant analysis

  2001cites this paper
• A new view on regularization theory

  2001cites this paper
• Distributed fault tolerance in optimal interpolative nets

  2001cites this paper
• Influence of noise on discrete time backpropagation trained networks

  2001cites this paper
• An Interpolation Technique for Learning with Sparse Data

  2000cites this paper
• 8 VLQJ WKH + HUPLWH 5 HJUHVVLRQ ) RUPXOD WR ' HVLJQ D 1 HXUDO $ UFKLWHFWXUH ZLWK $ XWRPDWLF / HDUQLQJ RI WKH 3 + LGGHQ ́ $ FWLYDWLRQ ) XQFWLRQV

  2000cites this paper
• Classifier's Complexity Control while Training Multilayer Perceptrons

  2000cites this paper
• Using the Hermite Regression Formula to Design a Neural Architecture with Automatic Learning of the "Hidden" Activation Functions

  1999cites this paper
• Regularization and Model Selection in the Context of Density Estimation

  1999cites this paper
• Inversion of feedforward neural networks: algorithms and applications

  1999cites this paper
• Noisy recurrent neural networks: the continuous-time case

  1998cites this paper