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 ...

Kernel functions are vital ingredients of several machine learning (ML) algorithms but often incur substantial memory and computational costs. We introduce an approach to kernel approximation in ML ...

A support vector machine is a classification algorithm for binary classification problems and is based on the theory of structural risk minimization. Of course, SVM can also be extended to multiclass ...

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 ...

A regression problem is one where the goal is to predict a single numeric value. For example, you might want to predict the annual income of a person based on their sex, age, State where they live and ...

BPF is a powerful component in the Linux kernel and the tools that make use of it are vastly varied and numerous. In this article we examine the general usefulness of BPF and guide you on a path ...

It used to be that building the Linux kernel was not easy. Testing and debugging were even worse. Nowadays, it is reasonably easy to build a custom kernel and test or debug it using virtualization.

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.

Back in the early 1990s, I worked in a large telecoms research lab, as part of the Advanced Local Loop group. Our problem domain was the “last mile”—getting services to peoples’ homes. One of my ...

All Linux distributions provide a wide range of network applications—from dæmons that provide a variety of services such as WWW, mail and SSH to client programs that access one or more of these ...