Abstract: A fast gradient-descent (FGD) method is proposed for far-field pattern synthesis of large antenna arrays. Compared with conventional gradient-descent (GD) methods for pattern synthesis where ...
Abstract: We study the convergence of minimum error entropy (MEE) algorithms when they are implemented by gradient descent. This method has been used in practical ...
Linear regression is the most fundamental machine learning technique to create a model that predicts a single numeric value. One of the three most common techniques to train a linear regression model ...
Understand what is Linear Regression Gradient Descent in Machine Learning and how it is used. Linear Regression Gradient Descent is an algorithm we use to minimize the cost function value, so as to ...
ABSTRACT: Artificial deep neural networks (ADNNs) have become a cornerstone of modern machine learning, but they are not immune to challenges. One of the most significant problems plaguing ADNNs is ...
Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses stochastic gradient descent, one of two ...
ABSTRACT: The study applies a Kalman filter (KF) to Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models to create a hybrid model, to estimate the parameters of the GARCH model in ...
This C library provides efficient implementations of linear regression algorithms, including support for stochastic gradient descent (SGD) and data normalization techniques. It is designed for easy ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results