Abstract: This paper investigates the momentum of athletes using a combination of linear regression and BP neural network algorithms. Firstly, we quantify momentum based on players’ winning streaks, ...
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Linear regression using gradient descent explained simply
Understand what is Linear Regression Gradient Descent in Machine Learning and how it is used. Linear Regression Gradient ...
Background Prehospital delays remain critical barriers to timely acute coronary syndrome (ACS) care, particularly for ...
Objective To examine whether a multicomponent commercial fitness app with very small (‘micro’) financial incentives (FI) ...
Based Detection, Linguistic Biomarkers, Machine Learning, Explainable AI, Cognitive Decline Monitoring Share and Cite: de Filippis, R. and Al Foysal, A. (2025) Early Alzheimer’s Disease Detection from ...
You chose selected. Each dot here represents a single video about selected. While you’re on the app, TikTok tracks how you interact with videos. It monitors your watch time, the videos you like, the ...
Abstract: Mixed linear regression (MLR) models nonlinear data as a mixture of linear components. When noise is Gaussian, the Expectation-Maximization (EM) algorithm is commonly used for maximum ...
Think about the last time you opened Netflix. Did you scroll through countless options or go with a recommended title? When you log into social media, do you decide what to see, or is your feed ...
Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the AdaBoost.R2 algorithm for regression problems (where the goal is to predict a single numeric value). The ...
Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of decision tree regression using the C# language. Unlike most implementations, this one does not use recursion ...
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