Nature

Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials

Modern whole-genome prediction (WGP) frameworks that focus on multi-environment trials (MET) integrate large-scale genomics, phenomics, and envirotyping data. However, the more complex the statistical ...
Nature

Kernel learning assisted synthesis condition exploration for ternary spinel

Mixed metal oxides (MMOs) exhibit excellent catalytic activity in reactions such as the oxygen evolution reaction 1,2,3 and hydrogen evolution reaction 4,5. MMOs can be composed of a wide selection of ...
Phys.org

Scientists introduce new method for machine learning classifications in quantum computing

Quantum information scientists have introduced a new method for machine-learning classifications in quantum computing. The non-linear quantum kernels in a quantum binary classifier provide new ...
Visual Studio Magazine

Kernel Ridge Regression with Cholesky Inverse Training Using JavaScript

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 ...
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 ...