🔬 Science 6h ago

Hidden geometry explains why kernel methods separate complex data so well

Phys.org
Phys.org science news
View Channel →
Hidden geometry explains why kernel methods separate complex data so well
Source ↗ 👁 0 💬 0
Are two sets of data genuinely different, or is it because of randomness? This question, known as the two-sample testing problem, becomes notoriously difficult in modern datasets, because they are often high-dimensional, complex, and differences between them can take countless subtle forms.

Comments (0)

Sign in to join the discussion

More Like This

More people with disabilities are seeking work, report reveals
Phys.org - latest science and technology news stories · 1h ago
We Surveyed Scientists About Aliens. Their Answers Were Revealing.
ScienceAlert · 1h ago
Q&A: When is screen time healthy and when is it not?
Medical Xpress - latest medical and health news stories · 1h ago
Oak Trees Outsmart Caterpillars With a Brilliant Spring Trick
SciTechDaily · 1h ago
MLB swing-tracking data helps researchers examine baseball's long-debated two-strike approach
Phys.org - latest science and technology news stories · 2h ago
Online therapy cuts insomnia and anxiety in adults 65 and older, trial suggests
Medical Xpress - latest medical and health news stories · 2h ago