BR—Bayesian Regularization training, LM -Levenberg-Marquardt training algorithm, SCG—Scaled Conjugate Gradient, the number in the brackets indicates the size of the time window delay for input and output, (2) two past values included, or (1) single past value included, evaluated for pine data set, see Fig 2.</p
A modified conjugate gradient algorithm is proposed which uses a gradient average window to pro-vide...
We consider a learning algorithm generated by a regularization scheme with a concave regularizer for...
A supervised learning algorithm (Scaled Conjugate Gradient, SCG) with superlinear convergence rate i...
BR—Bayesian Regularization training, LM -Levenberg-Marquardt training algorithm, SCG—Scaled Conjugat...
We describe a new algorithm providing regularized training of the extreme learning machine (ELM) tha...
Effect of the number of neurons on the performance of Levenberg–Marquardt (L-M), Bayesian Regulariza...
Levenberg Marquardt algorithm is used for training feedforward neural networks because of the effect...
Mean squared error based on the number of neurons using Levenberg–Marquardt (L-M), Bayesian Regulari...
<p>A) A two-dimensional example illustrate how a two-class classification between the two data sets ...
Predicted versus observed antler beam diameter and length for test dataset by Linear model and Leven...
Neural network is widely used for image classification problems, and is proven to be effective with ...
We consider supervised learning in the presence of very many irrelevant features, and study two diff...
<p>This figure illustrates the effectiveness of the weighted regularization (prior knowledge) at sim...
Pearson correlation coefficients between observed and predicted antler beam diameter and length base...
In this paper we explore different strategies to guide backpropagation algorithm used for training a...
A modified conjugate gradient algorithm is proposed which uses a gradient average window to pro-vide...
We consider a learning algorithm generated by a regularization scheme with a concave regularizer for...
A supervised learning algorithm (Scaled Conjugate Gradient, SCG) with superlinear convergence rate i...
BR—Bayesian Regularization training, LM -Levenberg-Marquardt training algorithm, SCG—Scaled Conjugat...
We describe a new algorithm providing regularized training of the extreme learning machine (ELM) tha...
Effect of the number of neurons on the performance of Levenberg–Marquardt (L-M), Bayesian Regulariza...
Levenberg Marquardt algorithm is used for training feedforward neural networks because of the effect...
Mean squared error based on the number of neurons using Levenberg–Marquardt (L-M), Bayesian Regulari...
<p>A) A two-dimensional example illustrate how a two-class classification between the two data sets ...
Predicted versus observed antler beam diameter and length for test dataset by Linear model and Leven...
Neural network is widely used for image classification problems, and is proven to be effective with ...
We consider supervised learning in the presence of very many irrelevant features, and study two diff...
<p>This figure illustrates the effectiveness of the weighted regularization (prior knowledge) at sim...
Pearson correlation coefficients between observed and predicted antler beam diameter and length base...
In this paper we explore different strategies to guide backpropagation algorithm used for training a...
A modified conjugate gradient algorithm is proposed which uses a gradient average window to pro-vide...
We consider a learning algorithm generated by a regularization scheme with a concave regularizer for...
A supervised learning algorithm (Scaled Conjugate Gradient, SCG) with superlinear convergence rate i...