<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Logistic Regression | L.E.R Academic</title><link>https://rongyi.ai/tag/logistic-regression/</link><atom:link href="https://rongyi.ai/tag/logistic-regression/index.xml" rel="self" type="application/rss+xml"/><description>Logistic Regression</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© 2022 Yi Rong</copyright><lastBuildDate>Tue, 22 Feb 2022 15:52:59 +0800</lastBuildDate><image><url>https://rongyi.ai/media/icon_hucf03f274847a1149dd55649cb0f12563_327173_512x512_fill_lanczos_center_3.png</url><title>Logistic Regression</title><link>https://rongyi.ai/tag/logistic-regression/</link></image><item><title>CSE251A Project 2: Coordinate Descent for Logistic Regression</title><link>https://rongyi.ai/report/cse251a-p2-report/</link><pubDate>Tue, 22 Feb 2022 15:52:59 +0800</pubDate><guid>https://rongyi.ai/report/cse251a-p2-report/</guid><description>&lt;p>In this report, we discuss our attempt to use a better strategy to pick the direction of coordinate descent at each step rather than random selection. At each iteration, we pick the gradient direction with the largest absolute value, and shows that our method outperforms the random coordinate descent in terms of convergence speed.&lt;/p></description></item></channel></rss>