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<rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:trackback="http://madskills.com/public/xml/rss/module/trackback/" xmlns:wfw="http://wellformedweb.org/CommentAPI/" xmlns:slash="http://purl.org/rss/1.0/modules/slash/"><channel><title>shahana's Blog - 数据挖掘</title><link>http://myself.pub/</link><description>Good Luck To You! - </description><generator>RainbowSoft Studio Z-Blog 1.8 Walle Build 100427</generator><language>zh-CN</language><copyright>Copyright shahana's WebSite. Some Rights Reserved.Record number:京ICP备19041645号.</copyright><pubDate>Sun, 17 May 2026 05:34:26 +086</pubDate><item><title>数据挖掘十大经典算法（包括各自优缺点 / 适用数据场景）</title><author>sh1218@126.com (zbloger)</author><link>http://myself.pub/数据挖掘学习/2.html</link><pubDate>Thu, 19 Sep 2019 14:47:28 +086</pubDate><guid>http://myself.pub/数据挖掘学习/2.html</guid><description><![CDATA[<p>本文主要分析皆来自其他资料，借用较为权威的总结来对本人已经学习的这些经典算法做一个极为精简的概述（根据自身经验有一定修改），另外同时附上机器学习实战中作者对各种算法</p>]]></description><category>数据挖掘</category><comments>http://myself.pub/数据挖掘学习/2.html#comment</comments><wfw:comment>http://myself.pub/</wfw:comment><wfw:commentRss>http://myself.pub/feed.asp?cmt=2</wfw:commentRss><trackback:ping>http://myself.pub/cmd.asp?act=tb&amp;id=2&amp;key=eb9032b2</trackback:ping></item></channel></rss>
