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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Cascading Randomized Weighted Majority: A New Online Ensemble Learning Algorithm · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1403.0388/</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/1403.0388/feed.xml" rel="self" type="application/rss+xml" /><item><title>Cascading Randomized Weighted Majority: A New Online Ensemble Learning Algorithm — mentioned by eternalban</title><link>https://news.ycombinator.com/item?id=15902617</link><guid isPermaLink="false">urn:hackerpapers:mention:1403.0388:15902617</guid><pubDate>Tue, 12 Dec 2017 02:37:51 GMT</pubDate><description>&lt;p&gt;Thanks for the link. Very interesting. I found this [1] from 2015. Reading your cite, the practical issue seems to me to be that the optimizer&amp;#x27;s memory footprint costs may in fact negate any benefit (e.g. ~40% over LRU) obtained in reducing cache misses. My gut feeling is that this approach (for online systems) may work best with a hardware component (a card hosting the &amp;#x27;experts&amp;#x27; and their virtual model e.g. the &amp;quot;virtual cache&amp;quot;). The distributed variant also seems worth exploring. [1]: https://arxiv.org/pdf/1403.0388.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1403.0388/"&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>
