Support vector regression can predict numeric values effectively, and this article shows how to implement and train a kernel SVR model in C# using stochastic sub-gradient descent.

A little-known Miami-based startup called Subquadratic emerged from stealth on Tuesday with a sweeping claim: that it has built the first large language model to fully escape the mathematical ...

Waterloo scientists have developed a new way to understand how the universe began, and it could change what we know about the Big Bang and the earliest moments of cosmic history. Their work suggests ...

Genomic prediction and design require models capable of integrating local sequence features with long-range regulatory dependencies. Nucleotide Transformer v3 (NTv3) is a multi-species genomic ...

Kernel ridge regression (KRR) is a regression technique for predicting a single numeric value and can deliver high accuracy for complex, non-linear data. KRR combines a kernel function (most commonly ...

ABSTRACT: This study investigates projectile motion under quadratic air drag, focusing on mass-dependent dynamics using the Runge-Kutta (RK4) method implemented in FreeMat. Quadratic drag, predominant ...

Function Health, a startup that offers lab testing and body scans for early disease detection, scored nearly $300 million in series B funding, boosting its valuation to $2.5 billion. Redpoint Ventures ...

Researchers at the University of California, Los Angeles (UCLA) have developed an optical computing framework that performs large-scale nonlinear computations using linear materials. Reported in ...

Abstract: This article proposes linear quadratic controllers for unknown nonlinear systems with noise. Scenarios with unknown underlying nonlinear dynamics but measurable system states are considered.

Abstract: The quadratic polynomial regression model with L2 regularization is developed by combining the nonlinear fitting ability of polynomial regression and the regularization feature of ridge ...