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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Persistent-Homology-based Machine Learning and its Applications -- A Survey · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1811.00252/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1811.00252/feed.xml" rel="self" type="application/rss+xml" /><item><title>Persistent-Homology-based Machine Learning and its Applications -- A Survey — mentioned by del_operator</title><link>https://news.ycombinator.com/item?id=19498100</link><guid isPermaLink="false">urn:hackerpapers:mention:1811.00252:19498100</guid><pubDate>Wed, 27 Mar 2019 04:03:22 GMT</pubDate><description>&lt;p&gt;I’ve also been reading about persistence more in the Edelsbrunner and Harer Computational Toplogy text, but glancing at this book on page 104 I see good examples and intuitive build up. I found this overview I liked for persistence in ML https://arxiv.org/pdf/1811.00252.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1811.00252/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>math.AT</category></item></channel></rss>
