<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[xpplearnc的专栏]]></title><description><![CDATA[我要证明你错了]]></description><link>https://blog.youkuaiyun.com/xpplearnc</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; xpplearnc]]></copyright><item><title><![CDATA[VS2015用起来还是有很多便捷性的-不断补充更新]]></title><link>https://blog.youkuaiyun.com/xpplearnc/article/details/87184666</link><guid>https://blog.youkuaiyun.com/xpplearnc/article/details/87184666</guid><author>xpplearnc</author><pubDate>Wed, 13 Feb 2019 15:37:08 +0800</pubDate><description><![CDATA[VS里面默认的注释快捷键是

注释：Ctrl+K,Ctrl+C

取消注释：Ctrl+K,Ctrl+U

组合快捷键用着太麻烦，使用下面的方法可以修改一下

 

1.工具---&amp;gt;选项--&amp;gt;环境---&amp;gt;键盘,搜索注释



 



 



 

2.按键盘设置自定义的全局快捷键，点击分配（这一步必须要有，否则设置无效），点击确定就可以了

 



点击分配再点击确定即可。
...]]></description><category></category></item><item><title><![CDATA[IPOL Journal · Image Processing On Line--一个包含文章和源码的在线杂志网站]]></title><link>https://blog.youkuaiyun.com/xpplearnc/article/details/53171417</link><guid>https://blog.youkuaiyun.com/xpplearnc/article/details/53171417</guid><author>xpplearnc</author><pubDate>Tue, 15 Nov 2016 15:05:43 +0800</pubDate><description><![CDATA[http://www.ipol.im/]]></description><category></category></item><item><title><![CDATA[opencv3.1中svm的使用范例]]></title><link>https://blog.youkuaiyun.com/xpplearnc/article/details/51909187</link><guid>https://blog.youkuaiyun.com/xpplearnc/article/details/51909187</guid><author>xpplearnc</author><pubDate>Thu, 14 Jul 2016 14:39:42 +0800</pubDate><description><![CDATA[#include 
#include 
#include "opencv2/imgcodecs.hpp"
#include 
#include 

using namespace cv;
using namespace cv::ml;

int main(int, char**)
{
    // Data for visual representation
    int width = 512]]></description><category></category></item><item><title><![CDATA[机器学习算法概览]]></title><link>https://blog.youkuaiyun.com/xpplearnc/article/details/45666971</link><guid>https://blog.youkuaiyun.com/xpplearnc/article/details/45666971</guid><author>xpplearnc</author><pubDate>Tue, 12 May 2015 11:08:17 +0800</pubDate><description><![CDATA[http://www.valseonline.org/thread-152-1-1.html





http://blog.youkuaiyun.com/stdcoutzyx/article/details/44501797


本文是翻译文章，但我并未逐字句的翻译，而是有所删减，并加入了一些自己的补充。

转载请注明：http://blog.youkuaiyun.com/stdcoutzyx/]]></description><category></category></item><item><title><![CDATA[百度课程的地址]]></title><link>https://blog.youkuaiyun.com/xpplearnc/article/details/42740661</link><guid>https://blog.youkuaiyun.com/xpplearnc/article/details/42740661</guid><author>xpplearnc</author><pubDate>Thu, 15 Jan 2015 14:11:51 +0800</pubDate><description><![CDATA[http://wenku.baidu.com/course/index]]></description><category></category></item><item><title><![CDATA[傅里叶分析]]></title><link>https://blog.youkuaiyun.com/xpplearnc/article/details/42169205</link><guid>https://blog.youkuaiyun.com/xpplearnc/article/details/42169205</guid><author>xpplearnc</author><pubDate>Fri, 26 Dec 2014 11:15:01 +0800</pubDate><description><![CDATA[傅里叶分析究竟是干什么用的?这段相对比较枯燥,已经知道了的同学可以直接跳到下一个分割线。


先说一个最直接的用途。无论听广播还是看电视,我们一定对一个词不陌生——频道。频道频道,就是频率的通道,不同的频道就是将不同的频率作为一个通道来进行信息传输。下面大家尝试一件事:


先在纸上画一个sin(x),不一定标准,意思差不多就行。不是很难吧。


好,接下去画一个sin(3x)+]]></description><category></category></item><item><title><![CDATA[memset用法详解]]></title><link>https://blog.youkuaiyun.com/xpplearnc/article/details/41805169</link><guid>https://blog.youkuaiyun.com/xpplearnc/article/details/41805169</guid><author>xpplearnc</author><pubDate>Mon, 08 Dec 2014 16:25:42 +0800</pubDate><description><![CDATA[http://www.cnblogs.com/PegasusWang/archive/2013/01/20/2868824.html




1。void *memset(void *s,int c,size_t n)
总的作用：将已开辟内存空间 s 的首 n 个字节的值设为值 c。

2。例子
＃include

void main(){
char *s="Golden ]]></description><category></category></item><item><title><![CDATA[比较独到的pca理解]]></title><link>https://blog.youkuaiyun.com/xpplearnc/article/details/41577299</link><guid>https://blog.youkuaiyun.com/xpplearnc/article/details/41577299</guid><author>xpplearnc</author><pubDate>Fri, 28 Nov 2014 16:22:17 +0800</pubDate><description><![CDATA[http://blog.youkuaiyun.com/xiaojidan2011/article/details/11595869

PCA（Principal Component Analysis）是一种常用的数据分析方法。PCA通过线性变换将原始数据变换为一组各维度线性无关的表示，可用于提取数据的主要特征分量，常用于高维数据的降维。网上关于PCA的文章有很多，但是大多数只描述了PCA的分析过程，而没有]]></description><category></category></item><item><title><![CDATA[安装以及配置OpenCv]]></title><link>https://blog.youkuaiyun.com/xpplearnc/article/details/41576867</link><guid>https://blog.youkuaiyun.com/xpplearnc/article/details/41576867</guid><author>xpplearnc</author><pubDate>Fri, 28 Nov 2014 15:08:47 +0800</pubDate><description><![CDATA[http://blog.youkuaiyun.com/morewindows/article/details/8225783/


【OpenCV第一篇】安装OpenCV


本篇主要介绍如何下载OpenCV安装程序，如何在VS2008下安装配置OpenCV，文章最后还介绍了一个使用OpenCV的简单小例子。

《OpenCV入门指南》系列文章地址：http://blog.youkuaiyun.com/m]]></description><category></category></item><item><title><![CDATA[pca概念整理]]></title><link>https://blog.youkuaiyun.com/xpplearnc/article/details/41487371</link><guid>https://blog.youkuaiyun.com/xpplearnc/article/details/41487371</guid><author>xpplearnc</author><pubDate>Tue, 25 Nov 2014 19:14:24 +0800</pubDate><description><![CDATA[http://blog.youkuaiyun.com/jinshengtao/article/details/18448355




本文将分别介绍特征值分解、奇异值分解、及PCA的相关理论概念。

文章末尾将给出Householder矩阵变换、QR算法求解特征值、特征向量的代码







一、特征值与特征向量的几何意义



1.     矩阵乘法

在介绍特]]></description><category></category></item><item><title><![CDATA[特征值分解、奇异值分解、PCA概念整理]]></title><link>https://blog.youkuaiyun.com/xpplearnc/article/details/41479431</link><guid>https://blog.youkuaiyun.com/xpplearnc/article/details/41479431</guid><author>xpplearnc</author><pubDate>Tue, 25 Nov 2014 11:51:23 +0800</pubDate><description><![CDATA[http://blog.youkuaiyun.com/jinshengtao/article/details/18448355






本文将分别介绍特征值分解、奇异值分解、及PCA的相关理论概念。

文章末尾将给出Householder矩阵变换、QR算法求解特征值、特征向量的代码

其中，特征值分解、奇异值分解的相关内容，转载自：

http://www.cnblogs.com]]></description><category></category></item><item><title><![CDATA[PCA人脸识别学习及C语言实现]]></title><link>https://blog.youkuaiyun.com/xpplearnc/article/details/41446183</link><guid>https://blog.youkuaiyun.com/xpplearnc/article/details/41446183</guid><author>xpplearnc</author><pubDate>Mon, 24 Nov 2014 15:46:34 +0800</pubDate><description><![CDATA[http://blog.youkuaiyun.com/jinshengtao/article/details/18599165]]></description><category></category></item><item><title><![CDATA[pca的数学分析]]></title><link>https://blog.youkuaiyun.com/xpplearnc/article/details/41280987</link><guid>https://blog.youkuaiyun.com/xpplearnc/article/details/41280987</guid><author>xpplearnc</author><pubDate>Wed, 19 Nov 2014 13:52:12 +0800</pubDate><description><![CDATA[http://www.cnblogs.com/CBDoctor/archive/2011/10/29/2228756.html]]></description><category></category></item><item><title><![CDATA[【OpenCV入门教程之一】 安装OpenCV：OpenCV 3.0、OpenCV 2.4.8、OpenCV 2.4.9 +VS 开发环境配置]]></title><link>https://blog.youkuaiyun.com/xpplearnc/article/details/41205141</link><guid>https://blog.youkuaiyun.com/xpplearnc/article/details/41205141</guid><author>xpplearnc</author><pubDate>Mon, 17 Nov 2014 10:15:15 +0800</pubDate><description><![CDATA[http://blog.youkuaiyun.com/poem_qianmo/article/details/19809337]]></description><category></category></item><item><title><![CDATA[CodeProject的网站]]></title><link>https://blog.youkuaiyun.com/xpplearnc/article/details/40789395</link><guid>https://blog.youkuaiyun.com/xpplearnc/article/details/40789395</guid><author>xpplearnc</author><pubDate>Tue, 04 Nov 2014 18:31:43 +0800</pubDate><description><![CDATA[http://www.codeproject.com/Articles/17105/CBarChart]]></description><category></category></item><item><title><![CDATA[OpenCV中的HOG+SVM物体分类]]></title><link>https://blog.youkuaiyun.com/xpplearnc/article/details/40785833</link><guid>https://blog.youkuaiyun.com/xpplearnc/article/details/40785833</guid><author>xpplearnc</author><pubDate>Tue, 04 Nov 2014 15:34:15 +0800</pubDate><description><![CDATA[http://blog.youkuaiyun.com/yongshengsilingsa/article/details/7535496






这里总结网上自己找到的资料，搞一个简单的框架供大家参考一下。

OpenCV官方的SVM代码在http://www.opencv.org.cn/opencvdoc/2.3.2/html/doc/tutorials/ml/introduction]]></description><category></category></item><item><title><![CDATA[朴素贝叶斯的应用]]></title><link>https://blog.youkuaiyun.com/xpplearnc/article/details/40658379</link><guid>https://blog.youkuaiyun.com/xpplearnc/article/details/40658379</guid><author>xpplearnc</author><pubDate>Fri, 31 Oct 2014 17:50:01 +0800</pubDate><description><![CDATA[http://www.ruanyifeng.com/blog/2013/12/naive_bayes_classifier.html]]></description><category></category></item><item><title><![CDATA[阮一峰的网络日志地址]]></title><link>https://blog.youkuaiyun.com/xpplearnc/article/details/40658265</link><guid>https://blog.youkuaiyun.com/xpplearnc/article/details/40658265</guid><author>xpplearnc</author><pubDate>Fri, 31 Oct 2014 17:43:55 +0800</pubDate><description><![CDATA[http://www.ruanyifeng.com/blog/]]></description><category></category></item><item><title><![CDATA[LBP原理介绍以及算法实现]]></title><link>https://blog.youkuaiyun.com/xpplearnc/article/details/40658211</link><guid>https://blog.youkuaiyun.com/xpplearnc/article/details/40658211</guid><author>xpplearnc</author><pubDate>Fri, 31 Oct 2014 17:40:11 +0800</pubDate><description><![CDATA[http://blog.youkuaiyun.com/xidianzhimeng/article/details/19634573





没有看LBP之前觉得它很神秘，看完了之后也就那么回事，不过提出LBP的人确实很伟大！！ 

LBP（Local Binary Pattern，局部二值模式）是一种用来描述图像局部纹理特征的算子；它具有旋转不变性和灰度不变性等显著的优点。它是首先由T. O]]></description><category></category></item><item><title><![CDATA[海思Hi3516智能分析引擎应用介绍]]></title><link>https://blog.youkuaiyun.com/xpplearnc/article/details/40657725</link><guid>https://blog.youkuaiyun.com/xpplearnc/article/details/40657725</guid><author>xpplearnc</author><pubDate>Fri, 31 Oct 2014 17:12:53 +0800</pubDate><description><![CDATA[http://blog.youkuaiyun.com/cjsycyl/article/details/8029424]]></description><category></category></item></channel></rss>