<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[futu147的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/futu147</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; futu147]]></copyright><item><title><![CDATA[算法知识目录]]></title><link>https://blog.youkuaiyun.com/futu147/article/details/125896261</link><guid>https://blog.youkuaiyun.com/futu147/article/details/125896261</guid><author>futu147</author><pubDate>Wed, 20 Jul 2022 17:04:33 +0800</pubDate><description><![CDATA[算法相关内容]]></description><category></category></item><item><title><![CDATA[支持向量机 算法]]></title><link>https://blog.youkuaiyun.com/futu147/article/details/125895270</link><guid>https://blog.youkuaiyun.com/futu147/article/details/125895270</guid><author>futu147</author><pubDate>Wed, 20 Jul 2022 16:32:30 +0800</pubDate><description><![CDATA[支持向量机 : 可以进行升维,再对数据进行分类]]></description><category></category></item><item><title><![CDATA[线性回归算法]]></title><link>https://blog.youkuaiyun.com/futu147/article/details/125895247</link><guid>https://blog.youkuaiyun.com/futu147/article/details/125895247</guid><author>futu147</author><pubDate>Wed, 20 Jul 2022 16:31:19 +0800</pubDate><description><![CDATA[线性回归 : 梯度下降]]></description><category></category></item><item><title><![CDATA[随机森林算法]]></title><link>https://blog.youkuaiyun.com/futu147/article/details/125895220</link><guid>https://blog.youkuaiyun.com/futu147/article/details/125895220</guid><author>futu147</author><pubDate>Wed, 20 Jul 2022 16:30:37 +0800</pubDate><description><![CDATA[随机森林 : 样本随机,特征随机]]></description><category></category></item><item><title><![CDATA[朴素贝叶斯算法]]></title><link>https://blog.youkuaiyun.com/futu147/article/details/125895139</link><guid>https://blog.youkuaiyun.com/futu147/article/details/125895139</guid><author>futu147</author><pubDate>Wed, 20 Jul 2022 16:28:40 +0800</pubDate><description><![CDATA[朴素贝叶斯 : 高斯朴素贝叶斯, 多项式朴素贝叶斯, 伯努利朴素贝叶斯]]></description><category></category></item><item><title><![CDATA[逻辑回归算法]]></title><link>https://blog.youkuaiyun.com/futu147/article/details/125895111</link><guid>https://blog.youkuaiyun.com/futu147/article/details/125895111</guid><author>futu147</author><pubDate>Wed, 20 Jul 2022 16:27:03 +0800</pubDate><description><![CDATA[逻辑回归]]></description><category></category></item><item><title><![CDATA[决策树算法]]></title><link>https://blog.youkuaiyun.com/futu147/article/details/125895093</link><guid>https://blog.youkuaiyun.com/futu147/article/details/125895093</guid><author>futu147</author><pubDate>Wed, 20 Jul 2022 16:26:25 +0800</pubDate><description><![CDATA[决策树 : 信息增益, 信息增益率, gini系数]]></description><category></category></item><item><title><![CDATA[机器学习理论简介]]></title><link>https://blog.youkuaiyun.com/futu147/article/details/125895060</link><guid>https://blog.youkuaiyun.com/futu147/article/details/125895060</guid><author>futu147</author><pubDate>Wed, 20 Jul 2022 16:25:17 +0800</pubDate><description><![CDATA[机器学习理论简介]]></description><category></category></item><item><title><![CDATA[k最近邻算法]]></title><link>https://blog.youkuaiyun.com/futu147/article/details/125895018</link><guid>https://blog.youkuaiyun.com/futu147/article/details/125895018</guid><author>futu147</author><pubDate>Wed, 20 Jul 2022 16:24:02 +0800</pubDate><description><![CDATA[一种非参数、惰性学习方法，导致预测时速度慢当训练样本集较大时,会导致其计算开销高样本不平衡的时候，对稀有类别的预测准确率低KNN模型的可解释性不强。]]></description><category></category></item><item><title><![CDATA[K-Means 算法]]></title><link>https://blog.youkuaiyun.com/futu147/article/details/125894991</link><guid>https://blog.youkuaiyun.com/futu147/article/details/125894991</guid><author>futu147</author><pubDate>Wed, 20 Jul 2022 16:23:16 +0800</pubDate><description><![CDATA[数据点到原型的某种距离作为优化的目标函数，利用函数求极值的方法得到迭代运算的调整规则。K-means算法以欧式距离作为相似度测度，它是求对应某一初始聚类中心向量V最优分类，使得**评价指*标J（目标函数）**最小算法采用**误差平方和**准则函数作为聚类准则函数K-Means算法过程★算法过程如下（1）从数据集中随机选取K个数据作为初始质心,质心点不一定是数据集中的数据.（2）对剩余的每个数据测量其到每个质心的距离，并把它归到最近的质心的类.质心不变重点n_clusters。.........]]></description><category></category></item><item><title><![CDATA[DBSCAN 算法]]></title><link>https://blog.youkuaiyun.com/futu147/article/details/125894935</link><guid>https://blog.youkuaiyun.com/futu147/article/details/125894935</guid><author>futu147</author><pubDate>Wed, 20 Jul 2022 16:21:59 +0800</pubDate><description><![CDATA[DBSCAN类的重要参数也分为两类，一类是DBSCAN算法本身的参数，一类是最近邻度量的参数epsDBSCAN算法参数，即我们的ϵ-邻域的距离阈值，和样本距离超过ϵ的样本点不在ϵ-邻域内。默认值是0.5DBSCAN算法参数，即样本点要成为核心对象所需要的ϵ-邻域的样本数阈值。默认值是5metric最近邻距离度量参数algorithm最近邻搜索算法参数，算法一共有三种，第一种是蛮力实现‘brute’，第二种是KD树实现‘kd_tree’，第三种是球树实现‘ball_tree’............]]></description><category></category></item><item><title><![CDATA[Adaboost算法]]></title><link>https://blog.youkuaiyun.com/futu147/article/details/125894830</link><guid>https://blog.youkuaiyun.com/futu147/article/details/125894830</guid><author>futu147</author><pubDate>Wed, 20 Jul 2022 16:18:33 +0800</pubDate><description><![CDATA[Adaboost 算法]]></description><category></category></item><item><title><![CDATA[单表查询-函数]]></title><link>https://blog.youkuaiyun.com/futu147/article/details/125152677</link><guid>https://blog.youkuaiyun.com/futu147/article/details/125152677</guid><author>futu147</author><pubDate>Mon, 06 Jun 2022 20:34:32 +0800</pubDate><description><![CDATA[mysql 函数]]></description><category></category></item></channel></rss>