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Deep learning regularization: Prevent overfitting effectively explained

Regularization in Deep Learning is very important to overcome overfitting. When your training accuracy is very high, but test ...
Scientific Research Publishing

Heart Disease Prediction: A Logistic Regression Approach ()

1 School of Computing and Data Science, Wentworth Institute of Technology, Boston, USA. 2 Department of Computer Science and Quantitative Methods, Austin Peay State University, Clarksville, USA. 3 ...
Visual Studio Magazine

Kernel Ridge Regression with Cholesky Inverse Training Using C#

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 the kernel matrix inverse (Cholesky ...
IEEE

Enhancing Generalization in Evolutionary Feature Construction for Symbolic Regression Through Vicinal Jensen Gap Minimization

Abstract: Genetic programming-based feature construction has achieved significant success in recent years as an automated machine learning technique to enhance learning performance. However, ...
Too Old to Operate

Comparing Regression Models and Traditional Formulas for IOL

The following is a summary of “Intraocular Lens Power Calculation – Comparing Big Data Approaches to Established Formulas,” published in the February 2025 issue of American Journal of Ophthalmology by ...
Scientific Research Publishing

Theil, H. (1950) A Rank-Invariant Method of Linear and Polynomial Regression Analysis. Indagationes Mathematicae, 12, 173.

ABSTRACT: In this work, we seek the relationship between the order of the polynomial model and the number of knots and intervals that we need to fit the splines regression model. Regression models ...