Microsoft

AutoJack: How a single page can RCE the host running your AI agent

Ongoing research into AI agent framework security identified an exploit chain in AutoGen Studio (AutoGen’s open-source prototyping user interface) that allows untrusted web content rendered by a ...
How-To Geek on MSN

4 CPUs that are officially too old for anything in 2026

If your CPU is on the list, I have bad news for you.
Nature

Support Vector Machine Parameter Optimization Techniques

Support Vector Machines (SVMs) are powerful supervised learning models whose predictive performance depends critically on the choice of hyperparameters, notably the regularisation constant (C) and ...
LinkedIn

Kernel Trick in Support Vector Machine

In the 23rd edition of Maths Behind AI series, i take you through the notion, and derivation of Kernel trick used in SVM. Kernel trick, at least for me, remained one of the most ambiguous topics in ...
Ars Technica

Linux kernel maintainers are following through on removing Intel 486 support

One point in favor of the sprawling Linux ecosystem is its broad hardware support—the kernel officially supports everything from ’90s-era PC hardware to Arm-based Apple Silicon chips, thanks to ...
GitHub

TNO Quantum: ML - Classifiers - Support Vector Machine

TNO Quantum provides generic software components aimed at facilitating the development of quantum applications. This package implements a scikit-learn compatible, (quantum) support vector machine.
Nature

The impact of the exponential Kernel’s bandwidth parameter on learning algorithms

Exponential kernels are widely recognized as an effective choice in kernel-based methods. In learning algorithms and artificial intelligence, choosing an appropriate bandwidth parameter for a kernel ...
Linux Journal

Linux Kernel 6.14: A Leap Forward in Intel and AMD CPU Support

Linux 6.14 extends support to Intel's forthcoming Panther Lake CPUs, incorporating thermal driver support for improved power efficiency and enabling Ultra-High Bit Rate (UHBR) modes via DisplayPort on ...
IEEE

Series Arc Fault Identification Based on a Novel Newton-Optimized Sparse Kernel Support Vector Machine

Abstract: Existing machine learning-based methods for series arc fault (SAF) identification still suffer from slow training speed when dealing with large-scale SAF datasets. For this reason, we ...
Frontiers

Evaluating machine learning pipelines for multimodal neuroimaging in small cohorts: an ALS case study

Advancements in machine learning hold great promise for the analysis of multimodal neuroimaging data. They can help identify biomarkers and improve diagnosis for various neurological disorders.
GitHub

Add Multiple Kernel Learning (MKL) for Support Vector Machines (SVM)

I propose adding a Multiple Kernel Learning (MKL) module for kernel optimization in kernel-based methods (such as SVM) to scikit-learn. MKL is a more advanced approach compared to GridSearchCV, ...