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 ...

Review re-maps multi-view learning into four supervised scenarios and three granular sub-tiers, delivering the first unified ...

Scientific knowledge advances through the interplay of empiricism and theory. Empirical observations of environmental ...

A scientist in Sweden has developed a new hybrid local features-based method using thermographs to identify faulty solar panels. A researcher from Sweden’s Jönköping University has proposed a machine ...

In food drying applications, machine learning has demonstrated strong capability in predicting drying rates, moisture ...

Researchers from Peking University have conducted a comprehensive systematic review on the integration of machine learning into statistical methods for disease risk prediction models, shedding light ...

On October 3-4, 2025,The Federal Reserve Bank of Philadelphia and the Center for Applied AI at the University of Chicago Booth School of Business are co-hosting a conference on Frontiers in Machine ...

No audio available for this content. High-precision GNSS applications, such as real-time displacement monitoring and vehicle navigation, rely heavily on resolving carrier-phase ambiguities. However, ...

Jordan Awan receives funding from the National Science Foundation and the National Institute of Health. He also serves as a privacy consultant for the federal non-profit, MITRE. In statistics and ...

Modern agriculture is a data-rich but decision-constrained domain, where traditional methods struggle to keep pace with ...