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<h3 id="‘剪辑KNN条件"><a href="#‘剪辑KNN条件" class="headerlink" title="‘剪辑KNN条件"></a>‘剪辑KNN条件</h3><p>如果样本足够多，就可以重复地执行剪辑程序， 以进一步提高分类性能。称为重复剪辑最近邻法。</p>
<p>KNN被看作一种从样本中获取<strong>最大后验概率</strong>的方法，</p>
<p>剪辑最近邻法可以推广到K-近邻法中，具体是先用k-NN进行剪辑，第二部用1nn算法进行分类 ，当类数增加时，该方法的效果会变得更好。</p>
<p>重复加急最近邻法条件是样本足够多</p>
<h3 id="马氏距离-欧氏距离-明氏距离"><a href="#马氏距离-欧氏距离-明氏距离" class="headerlink" title="马氏距离 欧氏距离 明氏距离"></a>马氏距离 欧氏距离 明氏距离</h3><p>P24</p>
<p>欧式距离具有平移和旋转不变性。</p>
<p>马氏距离对一切<strong>非奇异线性变换</strong>都是不变的。这说明他不受量纲选择的影响。并且平移不变。此外马氏距离对特征的相关性做了处理（计算表达式中有矢量集的样本协方差阵。）：对一切非奇异线性变<br>换都是不变的。即，具有坐标系比例、<br>旋转、平移不变性，并且从统计意义<br>上尽量去掉了<strong>分量间的相关性。</strong></p>
<p><img src="1576326533231.png" alt="1576326533231"></p>
<p><img src="1576326558464.png" alt="1576326558464"></p>
<h3 id="感知器算法"><a href="#感知器算法" class="headerlink" title="感知器算法"></a>感知器算法</h3><p>P89</p>
<h3 id="H-K算法优点"><a href="#H-K算法优点" class="headerlink" title="H-K算法优点"></a>H-K算法优点</h3><p><strong>181.基于二次准则函数的H-K算法较之于感知器算法的优点是（BD）?</strong></p>
<p>A.计算量小</p>
<p>B.可以判别问题是否线性可分</p>
<p>C.其解完全适用于非线性可分的情况</p>
<p>D.其解的适应性更好</p>
<h3 id="相似性测度-匹配测度"><a href="#相似性测度-匹配测度" class="headerlink" title="相似性测度 匹配测度"></a>相似性测度 匹配测度</h3><p>P28 Rao测度&Dice测度</p>
<h4 id="Rao"><a href="#Rao" class="headerlink" title="Rao"></a>Rao</h4><p><img src="1576225756744.png" alt="1576225756744"></p>
<p>等于(1-1)匹配测度和所考差的特征数之比</p>
<h4 id="Dice"><a href="#Dice" class="headerlink" title="Dice"></a>Dice</h4><p><img src="1576225955505.png" alt="1576225955505"></p>
<h3 id="fisher判别原理"><a href="#fisher判别原理" class="headerlink" title="fisher判别原理"></a>fisher判别原理</h3><p> 费歇（FISHER）判别思想是投影，使多维问题简化为一维问题来处理。选择一个适当的投影轴,使所有的样品点都投影到这个轴上得到一个投影值。对这个投影轴的方向的要求是：使每一类内的投影值所形成的类内离差尽可能小，而不同类间的投影值所形成的类间离差尽可能大。 </p>
<p> <img src="20170503190745365.png" alt="摘自周志华老师的机器学习"> </p>
<h3 id="判别域界面方程"><a href="#判别域界面方程" class="headerlink" title="判别域界面方程"></a>判别域界面方程</h3><h3 id="聚类分析的分类？"><a href="#聚类分析的分类？" class="headerlink" title="聚类分析的分类？"></a>聚类分析的分类？</h3><p>聚类分析有很多具体的算法,有的比较简单, 有的相对复杂和完善,但归纳起来就是三大类: 1、按最小距离原则简单聚类方法 2、按最小距离原则进行两类合并的方法 3、依据准则函数动态聚类方法 4 近邻函数法</p>
<h3 id="C均值算法影响因素"><a href="#C均值算法影响因素" class="headerlink" title="C均值算法影响因素"></a>C均值算法影响因素</h3><p>受到取定的<strong>类别数</strong>和<strong>初始聚类中心</strong>的影响，通常结果只是局部最优的，但其 方法简单，结果尚令人满意，故应用较多</p>
<p><img src="1576326581752.png" alt="1576326581752"></p>
<h3 id="层次聚类算法例题"><a href="#层次聚类算法例题" class="headerlink" title="层次聚类算法例题"></a>层次聚类算法例题</h3><h3 id="最小误判-最小损失"><a href="#最小误判-最小损失" class="headerlink" title="最小误判 最小损失"></a>最小误判 最小损失</h3><h3 id="为何提出最小损失准则"><a href="#为何提出最小损失准则" class="headerlink" title="为何提出最小损失准则"></a>为何提出最小损失准则</h3><p>从不同性质的错误会引起不同程度的损失这一考虑出发，我们有时宁肯扩大一些总 的错误率，也要使总的损失最小。这就提出 了最小损失准则的决策方法。</p>
<h3 id="最小误判有几种形式"><a href="#最小误判有几种形式" class="headerlink" title="最小误判有几种形式"></a>最小误判有几种形式</h3><p>四种</p>
<p><img src="1576311410009.png" alt="1576311410009"></p>
<h3 id="fisher主要计步骤与分类决策规则"><a href="#fisher主要计步骤与分类决策规则" class="headerlink" title="fisher主要计步骤与分类决策规则"></a>fisher主要计步骤与分类决策规则</h3><p>1，把来自两类omega_1/omega_2的训练样本集X分成omega_1对应的子集X_1和与omega_2对应的子集X_2</p>
<p>2 计算m_i 即平均数</p>
<p>3 计算总的类内离差阵S_w</p>
<p>4 计算S_w的逆矩阵S_w^-1</p>
<p>5 按u = S^-{1}_{w}(m_1 - m_2)</p>
<p>6 计算m一弯和y_t</p>
<p>7 分类 </p>
<p><img src="1576306181726.png" alt="1576306181726"></p>
<p><img src="1576310329922.png" alt="1576310329922"></p>
<h3 id="什么是线性判别函数"><a href="#什么是线性判别函数" class="headerlink" title="什么是线性判别函数"></a>什么是线性判别函数</h3><p> 特征的所在子区域可根据它的特征值代入界面方程的线性函数d(x)后的取值正负而确定，表示界面的线性函数d(x)称为线性判别函数。</p>
<h3 id="线性判决的几何意义"><a href="#线性判决的几何意义" class="headerlink" title="线性判决的几何意义"></a>线性判决的几何意义</h3><p> 运用已知类别的训练样本进行学习产生若干个代数界面d(x）=0,将特征空间划分成一些互不重叠的子区域，使不同的模式类或其主体在不同的子区域中</p>
<p><img src="1576325975691.png" alt="1576325975691"></p>
<h3 id="判决分类结果好坏的标准-一句话"><a href="#判决分类结果好坏的标准-一句话" class="headerlink" title="判决分类结果好坏的标准 一句话"></a>判决分类结果好坏的标准 一句话</h3><p>类内距离小， 类间距离大</p>
<h3 id="最小损失和最小误判的关系"><a href="#最小损失和最小误判的关系" class="headerlink" title="最小损失和最小误判的关系"></a>最小损失和最小误判的关系</h3><p>取0-1损失函数时，最小损失准则等价于最小误<br>判概率准则，此时的平均损失就是误判概率，使平<br>均损失最小即使误判概率最小。这也表明，最小误<br>判概率准则是最小损失准则的特例</p>
<h3 id="最大似然估计和矩估计的概念"><a href="#最大似然估计和矩估计的概念" class="headerlink" title="最大似然估计和矩估计的概念"></a>最大似然估计和矩估计的概念</h3><p>矩法估计是用样本(的统计)矩作为总体(理论)矩的估 值</p>
<p>最大似然估计需要知道概型，据估计不用</p>
<h3 id="聚类分析的使用条件，-条状分布应该用什么算法，-团状用什么算法"><a href="#聚类分析的使用条件，-条状分布应该用什么算法，-团状用什么算法" class="headerlink" title="聚类分析的使用条件， 条状分布应该用什么算法， 团状用什么算法"></a>聚类分析的使用条件， 条状分布应该用什么算法， 团状用什么算法</h3><p>团状用C均值， 条状用近邻 函数法</p>
<p><img src="1576131925604.png" alt="1576131925604"></p>
<h3 id="最小条件损失，-最小损失准则概念"><a href="#最小条件损失，-最小损失准则概念" class="headerlink" title="最小条件损失， 最小损失准则概念"></a>最小条件损失， 最小损失准则概念</h3><p>按照两害相权取其轻原则，所采取的决策应是在统计意义上由于误判而蒙受的损失最小。</p>
<h3 id="C均值重心选择-样本顺序影响因素，选类心的顺序"><a href="#C均值重心选择-样本顺序影响因素，选类心的顺序" class="headerlink" title="C均值重心选择 样本顺序影响因素，选类心的顺序"></a>C均值重心选择 样本顺序影响因素，选类心的顺序</h3><p><img src="1576306938202.png" alt="1576306938202"></p>
<h3 id="相似性测度-匹配距离测度相似测度-劳氏-戴氏"><a href="#相似性测度-匹配距离测度相似测度-劳氏-戴氏" class="headerlink" title="相似性测度 匹配距离测度相似测度 劳氏 戴氏"></a>相似性测度 匹配距离测度相似测度 劳氏 戴氏</h3><h3 id="模式识别系统构成"><a href="#模式识别系统构成" class="headerlink" title="模式识别系统构成"></a>模式识别系统构成</h3><p>特征提取 特征选择 学习和训练 分类识别</p>
<h3 id="层次聚类，算法结果影响的因素"><a href="#层次聚类，算法结果影响的因素" class="headerlink" title="层次聚类，算法结果影响的因素"></a>层次聚类，算法结果影响的因素</h3><p><img src="1576325917801.png" alt="1576325917801"></p>
<h3 id="两类方法处理多分类"><a href="#两类方法处理多分类" class="headerlink" title="两类方法处理多分类"></a>两类方法处理多分类</h3><h3 id="什么是模式识别，对象，特征空间。"><a href="#什么是模式识别，对象，特征空间。" class="headerlink" title="什么是模式识别，对象，特征空间。"></a>什么是模式识别，对象，特征空间。</h3><h4 id="模式识别概念"><a href="#模式识别概念" class="headerlink" title="模式识别概念"></a>模式识别概念</h4><p>根据研究对象的特征或属性，运用一定的 分析算法认定其类别，并且分类识别的结果应 尽可能地符合真实。</p>
<h4 id="特征矢量"><a href="#特征矢量" class="headerlink" title="特征矢量"></a>特征矢量</h4><p>一个分析对象的n个特征量测量值分别为x_1, x_2, … , x_n他们构成一个n维特征矢量x , x = (x_1, x_2, …, x_n)’</p>
<h4 id="特征空间。"><a href="#特征空间。" class="headerlink" title="特征空间。"></a>特征空间。</h4><p>各种不同取值的 的全体构成了 维空间，这 个 维空间称为特征空间，</p>
<h3 id="先验概率、后验概率、类概率密度"><a href="#先验概率、后验概率、类概率密度" class="headerlink" title="先验概率、后验概率、类概率密度"></a>先验概率、后验概率、类概率密度</h3><p><img src="1576313111625.png" alt="1576313111625"></p>
<p><img src="1576313128994.png" alt="1576313128994"></p>
<p><img src="1576313144936.png" alt="1576313144936"></p>
<p> </p>
<h3 id="贝叶斯公式"><a href="#贝叶斯公式" class="headerlink" title="贝叶斯公式"></a>贝叶斯公式</h3><p><img src="1576313170181.png" alt="1576313170181"></p>
<h3 id="NP判决与最小损失，最小误判的异同"><a href="#NP判决与最小损失，最小误判的异同" class="headerlink" title="NP判决与最小损失，最小误判的异同"></a>NP判决与最小损失，最小误判的异同</h3><p><img src="1576313430128.png" alt="1576313430128"></p>
<p><img src="1576326065205.png" alt="1576326065205"></p>
<h3 id="FROM试题"><a href="#FROM试题" class="headerlink" title="FROM试题"></a>FROM试题</h3><p><img src="1576326722883.png" alt="1576326722883"></p>
<p>模式的特性 ：可观察性、可区分性、相似性 </p>
<p><img src="1576328261504.png" alt="1576328261504"></p>
<p><img src="1576328667992.png" alt="1576328667992"></p>
<p><img src="1576328751605.png" alt="1576328751605"></p>
<p>答案：约束或限制</p>
<p><img src="1576328808890.png" alt="1576328808890"></p>
<p>答案 </p>
<p><img src="1576328849230.png" alt="1576328849230"></p>
<p>答案：聚类算法</p>
<p><img src="1576397778665.png" alt="1576397778665"></p>
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<p>统计行数，单词书与字符串数， 这里对单词的定义 比较宽松， 他是任何不包含空格、制表符或者换行符序列。下面这段程序使 UNIX系统中wc程序的骨干部分。</p>
<figure class="highlight c"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br></pre></td><td class="code"><pre><span class="line"><span class="meta">#<span class="meta-keyword">include</span><span class="meta-string"><stdio.h></span></span></span><br><span class="line"><span class="meta">#<span class="meta-keyword">define</span> IN 1 <span class="comment">/*inside a word*/</span></span></span><br><span class="line"><span class="meta">#<span class="meta-keyword">define</span> OUT 0 <span class="comment">/*outside a word*/</span></span></span><br><span class="line"><span class="comment">/* count lines, words, and characters in input*/</span></span><br><span class="line"><span class="function"><span class="keyword">int</span> <span class="title">main</span><span class="params">()</span></span>{</span><br><span class="line"> <span class="keyword">int</span> c, nl, nw, nc, state;</span><br><span class="line"> state = OUT;</span><br><span class="line"> nl = nw = nc = <span class="number">0</span>;</span><br><span class="line"> <span class="keyword">while</span>((c==getchar() != EOF)){</span><br><span class="line"> ++nc;</span><br><span class="line"> <span class="keyword">if</span>(c==<span class="string">'\n'</span>)</span><br><span class="line"> ++nl;</span><br><span class="line"> <span class="keyword">if</span>(c==<span class="string">' '</span>|| c==<span class="string">'\n'</span> ||c =<span class="string">'\t'</span>)</span><br><span class="line"> state==OUT;</span><br><span class="line"> <span class="keyword">else</span> <span class="keyword">if</span>(state==OUT){</span><br><span class="line"> state = IN;</span><br><span class="line"> ++nw;</span><br><span class="line"> }</span><br><span class="line"> <span class="built_in">printf</span>(<span class="string">"%d %d %d"</span>, nl, nw, nc);</span><br><span class="line"> }</span><br><span class="line">}</span><br></pre></td></tr></table></figure>
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