The gradient descent algorithm is a type of optimization algorithm that is widely used to solve machine learning algorithm model parameters. Through continuous iteration, it obtains the gradient of ...

Objective functions in deep learning algorithms are the main keys for optimizing the parameters of a network and can affect the quality of the denoised image significantly. Hence, this work examined ...

Loss Function: The technique of Boosting uses various loss functions. In case of Adaptive Boosting or AdaBoost, it minimises the exponential loss function that can make the algorithm sensitive to the ...

Gradient descent algorithms take the loss function and use partial derivatives to determine what each variable (weights and biases) in the network contributed to the loss value.

Gradient Boosting is a machine learning algorithm made up of Gradient descent and Boosting. Gradient Boosting has three primary components: additive model, loss function, and a weak learner; it ...

The NRRIDG method performs well for small to medium-sized problems and does not need many function, gradient, and Hessian calls. However, if the computation of the Hessian matrix is computationally ...