Somefun et al., (2021). NLSIG-COVID19Lab: A modern logistic-growth tool (nlogistic-sigmoid) for descriptively modelling the dynamics of COVID-19 pandemic process. Journal Open Source Software, 6(60), 3002, https://doi.org/10.21105/joss.03002

In this report, several experiments have been conducted on a spam data set with Logistic Regression based on Gradient Descent approach. First, the overfitting effect is shown with basic settings (vanilla version). Then Stochastic Gradient Descent and 2-Norm Regularization techniques are both implemented with demonstration of the benefits of these two methods in preventing overfitting. Besides, ...

We find the conditions under which a Riemannian manifold equipped with a closed threeform and a vector field define an on–shell N = (2, 2) supersymmetric gauged sigma model. The conditions are that the manifold admits a twisted generalized Kähler structure, that the vector field preserves this structure, and that a so–called generalized moment map exists for it. By a theorem in generalized comp...

We define a new subclass of univalent function based on Salagean differential operator and obtained the initial Taylor coefficients using the techniques of Briot-Bouquet differential subordination in association with the modified sigmoid function. Further we obtain the classical Fekete-Szego inequality results.

We consider the use of sigmoid functions for multistage detection in asynchronous code-division multiple-access (CDMA) systems. The sigmoid decision function for each stage of multistage detection is derived under the assumption that the residual noise which remains after the multiple-access interference (MAI) cancellation at a stage is Gaussian. It is suggested that the sigmoid function should...

Before getting into how unsupervised pre-training improves the performance of deep architecture, let’s first look into some basics. Let’s start with logistic regression, which is one of the first models for classification that is taught in machine learning. Logistic classification deals with the supervised learning problem of learning a mapping F : X → Y given a set of training points X = {x1 ....

Non-linear growth curves or growth curves that follow a specified non-linear function in time enable researchers to model complex developmental patterns with parameters that are easily interpretable. In this paper we describe how a variety of sigmoid curves can be fit using the Mplus structural modeling program and the non-linear mixed-effects modeling procedure NLMIXED in SAS. Using longitudin...

Superposition of sigmoid function over a finite time interval is shown to be equivalent to the linear combination of the solutions of a linearly parameterized system of logistic differential equations. Due to the linearity with respect to the parameters of the system, it is possible to design an effective procedure for parameter adjustment. Stability properties of this procedure are analyzed. S...

Rectifying neurons are more biologically plausible than logistic sigmoid neurons, which are themselves more biologically plausible than hyperbolic tangent neurons. However, the latter work better for training multi-layer neural networks than logistic sigmoid neurons. This paper shows that networks of rectifying neurons yield equal or better performance than hyperbolic tangent networks in spite ...