https://hackerpaper.kaipfister.de/p/1407.7502/Collected HN comments and submissions linking this paper.enMon, 02 Nov 2026 11:16:22 GMThttps://news.ycombinator.com/item?id=15406871urn:hackerpapers:mention:1407.7502:15406871Thu, 05 Oct 2017 05:43:46 GMT<p>Learning enough to make your own spin on things by mixing and matching parts is really easy once you understand the mathematical underpinnings of things. It&#x27;s true, many API-level things are opaque about how they work, but their theoretical foundations are usually possible to pick up. I guess a good way to do this is via an example. I had to build a system to hit the following criteria: * The data is scarce * The data is time series * The data follows a state machine * The data is noisy, and contains a signal that&#x27;s unique across all training samples Gluing together multiple domains of…</p><p><a href="https://hackerpaper.kaipfister.de/p/1407.7502/">Paper and discussion history</a>. A mention is not an endorsement.</p>stat.MLhttps://news.ycombinator.com/item?id=8141936urn:hackerpapers:submission:1407.7502:8141936Wed, 06 Aug 2014 11:28:40 GMT<p>Understanding Random Forests: From Theory to Practice</p><p></p><p>2 points; 0 discussion replies on HN. <a href="https://hackerpaper.kaipfister.de/p/1407.7502/">Paper and source history</a>. A submission is not an endorsement.</p>stat.ML