Kernel methods represent a cornerstone in modern machine learning, enabling algorithms to efficiently derive non-linear patterns by implicitly mapping data into high‐dimensional feature spaces. At the ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of Nadaraya-Watson kernel regression using the C# language. NW kernel regression is simple to implement and is ...

Large language models (LLMs) by themselves are less than meets the eye; the moniker “stochastic parrots” isn’t wrong. Connect LLMs to specific data for retrieval-augmented generation (RAG) and you get ...

Much of modern operating system functionality happens in and around the kernel. That’s a problem when you’re implementing monitoring and observability tools or adding low-level security tools because ...

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 stochastic gradient descent, one of two ...

The Linux Test Project (LTP) was developed to improve the Linux kernel by bringing automated testing to kernel design. Prior to the LTP, no formal testing environment was available to Linux developers ...

The Linux-Rust team has implemented new functions for kernel version 6.14 that promise a more stable use of core functions. Miguel Ojeda, the lead developer of Rust for Linux, has announced a number ...

where K 0 (·) is a kernel function, is the bandwidth, n is the sample size, and x i is the i th observation. The KERNEL option provides three kernel functions (K 0): normal, quadratic, and triangular.

Editor's Note: Linux remains an attractive option for embedded systems developers. In fact, industry surveys such as the Embedded Market Study by UBM (EDN's parent company) consistently show interest ...