We could approach the classification problem ignoring the fact that $y$ is discrete-valued, and use our old linear regression algorithm to try to predict $y$ given $x ...

And, to predict the target variable, we have to fit a logistic regression model based on these different features. In linear regression, we have used Squared Error ...

Logistic regression is a powerful statistical method that is used to model the probability that a set of explanatory (independent or predictor) variables predict data in an outcome (dependent or ...

Abstract: A new SPICE compatible memristor model is presented which is based on the solution to the well-known Logistic equation. Previously published first order function models are limited in their ...

A generalization of the common logistic function is developed, incorporating a non-unit saturation level, a non-zero intercept, and a non-symmetric shape. The dependence of the three generalized ...