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in this case one called "landmark detection", noting any unique features these algorithms have for this specific application, alongside other approaches and link to the general evolutionary algorithms page, is more appropriate - I just don't think the topic is wide enough to justify an article when the information is best presented as part of a more general one. I would argue for a rename or merge if there were anything of value in the article. Now I could be wrong about this, some topics such as
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deserve their own article because there are a lot of unique features when applying genetic algorithms to training neural nets, aside from the more general article on neural networks or evolutionary algorithm, even if they use standard algorithms like genetic algorithms as their base. Is the same true
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does it say that primary research papers don't add to notability. Of course they do. Lot's of people researching a topic is almost the definition of notability in a science subject. We must be cautious how we use primary sources, but they are not proscribed from being used at all, and even if they
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ok given an article for landmark detection doesn't yet exist, why don't we vote for this one to be renamed to "landmark detection", that way it still exists as a placeholder but is a more general article which will include the evolutionary algorithm approaches as part of it. If you agree with this I
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page or maybe a more general page on landmark detection, as it is just the application of standard algorithms such as partical swarm and genetic algorithms with maybe a few nuanced points. These algorithms have been applied to a wide range of fields and generally a mention in a more general article,
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I respect the effort here; I hadn't been able to dredge these up. Still, I am not convinced that a standalone article is warranted -- three papers with seventeen citations between them seems like it would make for one or two paragraphs at best. If I am wrong, and these end up being the bulwark of a
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page, also there is a lack of secondary sources covering this topic, suggesting it fails WP:NOTE (but even if there was I would argue it is best covered in the aforementioned article). There is nothing of value in the article in its current state so a merge is not necessary hence I vote delete.
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is "This paper provides an overview on evolutionary learning methods for the automated design and optimization of fuzzy logic controllers." It discusses landmark recognition. And it has 188 cites. So what additional requirement are you going to add to rule that one out as well?
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The sources linked are primary research papers and under
Knowledge (XXG):Notability general notability guidelines notability is established through coverage of secondary sources. Even if a secondary source existed, I would argue that this topic is best handled within a section of
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I didn't pick the example papers for the number of cites. I picked them because they explicitly had both "landmark detection" and "evolutionary|genetic algorithm" in the titles which made them unarguably on topic. But if number of cites is your concern then
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371:. I agree the article is in a poor state but I don't see any purpose in draftifying unless someone is volunteering to work on it. The creator has not been here for twelve years. Sending it to draft would just be slow-motion deletion.
502:, as those provide the most objective evidence of notability. There is no fixed number of sources required since sources vary in quality and depth of coverage, but multiple sources are generally expected. Sources do
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were, that does not stop them adding to notability. Notability does not fail because the tabloid press has not run sensationalist stories about it. Overview papers are not primary sources and are pretty much
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has 41 cites and included in the text "Landmark detection is a fundamental task in autonomous...". The first paper I linked has 28 cites alone according to gscholar so I don't know how you got to 17 total.
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Also again, even if we do establish evidence of lots of research covered by secondary sources such as review articles or certain textbooks, I still contest that this is best included within the
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680:: Per above, I think that this would address my notability concerns (obviously landmark detection is a thing which people do), so when I am back at the computer I will withdraw.
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I argue this article should be renamed to a more general "landmark detection" which will include the evolutionary approaches as well as other approaches as part of it.
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Sorry for the comment about moving goal posts, I thought it was the same user replying both times. You may be right that this is better covered in a general
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You are shifting the goalposts again. First you want sources, then sources with lots of cites. Now, so you say, primary research is ruled out. Nowhere in
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are real, but there's no indication here that this is a notable application, and the article is of very low quality. A
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Not sure how I can be shifting goalposts after my first comment. As for the part in WP:N it states as follows: "
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beautiful article on evolutionary algorithms for landmark detection, I will gladly withdraw my nomination.
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and as I'd argue there is nothing of value currently in that article it is best just deleted.
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The following discussion is an archived debate of the proposed deletion of the article below.
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Subsequent comments should be made on the appropriate discussion page (such as the article's
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on Google
Scholar -- only one, and it's a list likely scraped from Knowledge (XXG).
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here, or is it as I suspect, better covered by a more general article?
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Automatic Tuning of a Fuzzy Visual System Using
Evolutionary Algorithms
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article. There are certainly numerous review papers on the topic
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The above discussion is preserved as an archive of the debate.
361:"A Novel Genetic Algorithm for 3D Facial Landmark Localization"
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to generate a more thorough discussion and clearer consensus.
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43:). No further edits should be made to this page.
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279:Note: This discussion has been included in the
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56:. Hopefully some of these sources make it in!
128:Evolutionary Algorithm for Landmark Detection
80:Evolutionary Algorithm for Landmark Detection
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