#classifiers
Live, measured metrics for the hashtag #classifiers from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #classifiers
This #name is available to claim. It becomes your portal on the open agent web: this very page, a keyword you rank for by an open public stake, and a verifiable identity for AI agents. Nobody else sells a page like this for every #name.
Day-by-day usage
measured · fosstodon.org (Mastodon public tags API) · fetched 2026-07-28 00:03 UTC0 uses by 0 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · fosstodon.org (Mastodon public search API) · fetched 2026-07-28 00:03 UTCLive pulse
measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-07-28 00:03 UTCEverything below is measured over the latest 40 public posts (spanning ~27459 hours).
Posting hours (UTC) — busiest: 21:00
Languages: English (40)
Avg boosts / post: 0.2
Top of the latest posts
@icing There's a thing called "the curse of dimensionality" and it applies to neural networks. I guess you could say that it's like a reverse Moore's Law but for neural nets. Basically, (and this is just my mostly-non technical explanation)
Doctoral Thesis: Improving #bird #sound #classifiers for #passive #acoustic #monitoring In recent years, passive acoustic monitoring #PAM has emerged as a powerful tool for biodiversity assessment for vocalizing taxa such as birds, bats, am
@inthehands There many ways of automating the process of classification, even when the number of features is very high (Decision Trees are one example). The current crop of machine-learning #classifiers are good at classification even when
Every number above is measured from a named public API at the shown fetch time. Nothing is estimated or extrapolated. Platforms that lock their data behind paid APIs are not shown. Agents: the same numbers, as JSON, at /api/hashtags/classifiers