Scientific Research Publishing

Reproducing Kernel Hilbert Space Technique for Fourth-Order Singular Boundary Value Problems ()

Reproducing kernel Hilbert space method is utilized in this paper as an efficient approach to solve singular fourth order ...
Nature

Inherently interpretable position-aware convolutional motif kernel networks for biological sequencing data

Artificial neural networks show promising performance in detecting correlations within data that are associated with specific outcomes. However, the black-box nature of such models can hinder the ...
ZDNet

Kernel security now: Linux's unique method for securing code

The Linux Foundation's Greg Kroah-Hartman delivered a comprehensive talk this week on the current state and future challenges of Linux kernel security. Speaking at the Open Source Summit (OSS) Japan ...
Nature

A novel kernel based approach to arbitrary length symbolic data with application to type 2 diabetes risk

The application of supervised machine learning techniques to the medical domain has had significant impact in recent years, with clinical tasks in the areas of disease diagnosis, prognosis, and ...
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 ...
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 ...