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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Bayesian Nonlinear Support Vector Machines for Big Data · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1707.05532/</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/1707.05532/feed.xml" rel="self" type="application/rss+xml" /><item><title>Bayesian Nonlinear Support Vector Machines for Big Data — mentioned by cultus</title><link>https://news.ycombinator.com/item?id=18795701</link><guid isPermaLink="false">urn:hackerpapers:mention:1707.05532:18795701</guid><pubDate>Mon, 31 Dec 2018 17:36:32 GMT</pubDate><description>&lt;p&gt;There&amp;#x27;s been some work on variational Bayesian formulations of SVMs in the last few years. These can give actual uncertainty estimates and do automatic hyperparameter tuning. This one in particular is very cool: https://arxiv.org/pdf/1707.05532.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1707.05532/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item></channel></rss>
