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<!DOCTYPE html>
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<h1 id="什么是CCA"><a href="#什么是CCA" class="headerlink" title="什么是CCA"></a>什么是CCA</h1><p>典型关联分析(Canonical Correlation Analysis，以下简称CCA)是最常用的挖掘数据关联关系的算法之一。比如我们拿到两组数据，第一组是人身高和体重的数据，第二组是对应的跑步能力和跳远能力的数据。那么我们能不能说这两组数据是相关的呢？CCA可以帮助我们分析这个问题。将数据形式不同的矩阵映射成向量，从而可以比较他们之间的相关系数。多用在信息的检索</p>
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<h1 id="目录"><a href="#目录" class="headerlink" title="目录"></a>目录</h1><ul>
<li>线性可分二分类问题</li>
<li>线性可分多分类问题</li>
<li>线性可分多类别多分类问题</li>
<li>特征组合解决线性不可分问题</li>
<li>并行计算</li>
<li>在线学习</li>
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<h1 id="目的"><a href="#目的" class="headerlink" title="目的"></a>目的</h1><p>本人面过腾讯和头条的算法岗，可能是因为我简历上写了：<strong>使用过adaboost算法模型</strong>，所以无论是一面还是二面都被面试官问到有关<strong>adaboost算法原理</strong>的相关问题，而且变态的头条面试官居然还问到：<strong>为什么增加弱分类器，模型的效果变好？</strong>我当时是被问得一脸懵逼，所以写下这篇blog来梳理adaboost的相关知识点，本篇blog主要围绕三个点展开：</p>
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<li>以前向分布算法的角度理解adaboost</li>
<li>为什么增加基分类器模型的效果变好？</li>
<li>为什么我们要使用弱分类器作为基分类器而不是强分类器？</li>
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<h2 id="1-softmax"><a href="#1-softmax" class="headerlink" title="1.softmax"></a>1.softmax</h2><h3 id="a"><a href="#a" class="headerlink" title="(a)"></a>(a)</h3><p>$$<br>softmax(x + c) = \frac{e^{x_i+c}}{\sum_j e^{x_i+c}} = \frac{e^{x_i}e^c}{e^c\sum e^{x_i}} = softmax(x)<br>$$</p>
<p>这个技巧在代码编写非常常用，我们一般会令$c = -max\ x$来避免整数溢出</p>
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