MATLAB 2022a中基于2024年新算法的SVM参数优化研究：利用NRBO算法优化RBF函数惩罚与核参数，支持5分类任务的SVM性能评估,"MATLAB 2022a环境下应用新型NRBO算法优化
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MATLAB 2022a中基于2024年新算法的SVM参数优化研究：利用NRBO算法优化RBF函数惩罚与核参数，支持5分类任务的SVM性能评估,"MATLAB 2022a环境下应用新型NRBO算法优化SVM的RBF参数——以损失为适应度函数，实现5分类任务的高效与全局最优解",MATLAB2022a2024新算法牛顿-拉夫逊优化器（Newton-Raphson-based optimizer，NRBO）, 优化支持向量机SVM的RBF函数的惩罚参数与核参数，以SVM的损失为适应度函数，如果有自己适应度函数，有专门的函数直接替就行。并使用60%数据集训练SVM，40%数据集测试SVM的性能。SVM用于5分类，每一类是一个矩阵，矩阵的每一行为一个特征向量，一个特征向量有5个特征值，如下图。NRBO具有更快的收敛速度，有更好的全局最优解，2024最近发表的算法，你用就是创新,MATLAB2022a; 牛顿-拉夫逊优化器（NRBO）; SVM优化; 惩罚参数; 核参数; 适应度函数; 数据集分割; 5分类; 特征向量; 特征值; 快速收敛; 全局最优解; 创新算法,MATLAB 2 <link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/base.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/fancy.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90373210/2/raw.css" rel="stylesheet"/><div id="sidebar" style="display: none"><div id="outline"></div></div><div class="pf w0 h0" data-page-no="1" id="pf1"><div class="pc pc1 w0 h0"><img alt="" class="bi x0 y0 w1 h1" src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90373210/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">MATLAB 2022a<span class="_ _0"> </span><span class="ff2">与牛顿</span>-<span class="ff2">拉夫逊优化器在支持向量机参数优化中的应用</span></div><div class="t m0 x1 h2 y2 ff2 fs0 fc0 sc0 ls0 ws0">一<span class="ff3">、</span>引言</div><div class="t m0 x1 h2 y3 ff2 fs0 fc0 sc0 ls0 ws0">随着机器学习算法的不断发展<span class="ff4">，</span>支持向量机<span class="ff4">（<span class="ff1">SVM</span>）</span>已经成为一种重要的分类算法<span class="ff3">。</span>然而<span class="ff4">，<span class="ff1">SVM<span class="_ _0"> </span></span></span>的性</div><div class="t m0 x1 h2 y4 ff2 fs0 fc0 sc0 ls0 ws0">能往往受到其参数的影响<span class="ff4">，</span>如惩罚参数和核参数<span class="ff3">。</span>为了提高<span class="_ _1"> </span><span class="ff1">SVM<span class="_ _0"> </span></span>的分类性能<span class="ff4">，</span>我们可以通过优化这些</div><div class="t m0 x1 h2 y5 ff2 fs0 fc0 sc0 ls0 ws0">参数来实现<span class="ff3">。</span>近年来<span class="ff4">，</span>牛顿<span class="ff1">-</span>拉夫逊优化器<span class="ff4">（<span class="ff1">NRBO</span>）</span>作为一种高效的优化方法<span class="ff4">，</span>已经在各个领域得到</div><div class="t m0 x1 h2 y6 ff2 fs0 fc0 sc0 ls0 ws0">了广泛应用<span class="ff3">。</span>本文将探讨如何使用<span class="_ _1"> </span><span class="ff1">MATLAB 2022a<span class="_ _0"> </span></span>中的<span class="_ _1"> </span><span class="ff1">NRBO<span class="_ _0"> </span></span>来优化<span class="_ _1"> </span><span class="ff1">SVM<span class="_ _0"> </span></span>的<span class="_ _1"> </span><span class="ff1">RBF<span class="ff4">（</span></span>径向基函数<span class="ff4">）</span>函</div><div class="t m0 x1 h2 y7 ff2 fs0 fc0 sc0 ls0 ws0">数的惩罚参数与核参数<span class="ff3">。</span></div><div class="t m0 x1 h2 y8 ff2 fs0 fc0 sc0 ls0 ws0">二<span class="ff3">、</span>牛顿<span class="ff1">-</span>拉夫逊优化器<span class="ff4">（<span class="ff1">NRBO</span>）</span></div><div class="t m0 x1 h2 y9 ff2 fs0 fc0 sc0 ls0 ws0">牛顿<span class="ff1">-</span>拉夫逊优化器是一种基于牛顿法的迭代优化算法<span class="ff4">，</span>具有更快的收敛速度和更好的全局最优解<span class="ff3">。</span></div><div class="t m0 x1 h2 ya ff2 fs0 fc0 sc0 ls0 ws0">在<span class="_ _1"> </span><span class="ff1">2024<span class="_ _0"> </span></span>年<span class="ff4">，</span>一种新的<span class="_ _1"> </span><span class="ff1">NRBO<span class="_ _0"> </span></span>算法被提出并发表<span class="ff4">，</span>该算法在处理复杂问题时表现出色<span class="ff3">。</span>在<span class="_ _1"> </span><span class="ff1">SVM<span class="_ _0"> </span></span>参数优</div><div class="t m0 x1 h2 yb ff2 fs0 fc0 sc0 ls0 ws0">化中<span class="ff4">，<span class="ff1">NRBO<span class="_ _0"> </span></span></span>可以有效地寻找最佳的惩罚参数与核参数<span class="ff4">，</span>从而提高<span class="_ _1"> </span><span class="ff1">SVM<span class="_ _0"> </span></span>的分类性能<span class="ff3">。</span></div><div class="t m0 x1 h2 yc ff2 fs0 fc0 sc0 ls0 ws0">三<span class="ff3">、<span class="ff1">SVM<span class="_ _0"> </span></span></span>的<span class="_ _1"> </span><span class="ff1">RBF<span class="_ _0"> </span></span>函数及适应度函数</div><div class="t m0 x1 h2 yd ff1 fs0 fc0 sc0 ls0 ws0">SVM<span class="_ _0"> </span><span class="ff2">的<span class="_ _1"> </span></span>RBF<span class="_ _0"> </span><span class="ff2">函数是一种常用的核函数<span class="ff4">，</span>可以有效地处理非线性分类问题<span class="ff3">。</span>在参数优化过程中<span class="ff4">，</span>我们将</span></div><div class="t m0 x1 h2 ye ff1 fs0 fc0 sc0 ls0 ws0">SVM<span class="_ _0"> </span><span class="ff2">的损失作为适应度函数<span class="ff3">。</span>当使用<span class="_ _1"> </span></span>NRBO<span class="_ _0"> </span><span class="ff2">进行优化时<span class="ff4">，</span>我们可以通过<span class="_ _1"> </span></span>MATLAB<span class="_ _0"> </span><span class="ff2">提供的函数直接替换</span></div><div class="t m0 x1 h2 yf ff2 fs0 fc0 sc0 ls0 ws0">适应度函数<span class="ff3">。</span></div><div class="t m0 x1 h2 y10 ff2 fs0 fc0 sc0 ls0 ws0">四<span class="ff3">、</span>数据集划分与<span class="_ _1"> </span><span class="ff1">SVM<span class="_ _0"> </span></span>训练<span class="ff3">、</span>测试</div><div class="t m0 x1 h2 y11 ff2 fs0 fc0 sc0 ls0 ws0">我们将数据集按照<span class="_ _1"> </span><span class="ff1">60%</span>训练<span class="ff3">、<span class="ff1">40%</span></span>测试的比例进行划分<span class="ff3">。</span>其中<span class="ff4">，<span class="ff1">60%</span></span>的数据用于训练<span class="_ _1"> </span><span class="ff1">SVM<span class="ff4">，</span>40%</span>的数据</div><div class="t m0 x1 h2 y12 ff2 fs0 fc0 sc0 ls0 ws0">用于测试<span class="_ _1"> </span><span class="ff1">SVM<span class="_ _0"> </span></span>的性能<span class="ff3">。</span>每一类数据都是一个矩阵<span class="ff4">，</span>矩阵的每一行为一个特征向量<span class="ff4">，</span>一个特征向量有<span class="_ _1"> </span><span class="ff1">5</span></div><div class="t m0 x1 h2 y13 ff2 fs0 fc0 sc0 ls0 ws0">个特征值<span class="ff3">。</span>在训练过程中<span class="ff4">，</span>我们使用<span class="_ _1"> </span><span class="ff1">NRBO<span class="_ _0"> </span></span>优化的参数来初始化<span class="_ _1"> </span><span class="ff1">SVM<span class="ff4">，</span></span>并通过迭代优化过程不断调整</div><div class="t m0 x1 h2 y14 ff2 fs0 fc0 sc0 ls0 ws0">参数<span class="ff4">，</span>以获得最佳的分类性能<span class="ff3">。</span></div><div class="t m0 x1 h2 y15 ff2 fs0 fc0 sc0 ls0 ws0">五<span class="ff3">、<span class="ff1">NRBO<span class="_ _0"> </span></span></span>优化<span class="_ _1"> </span><span class="ff1">SVM<span class="_ _0"> </span></span>参数的过程</div><div class="t m0 x1 h2 y16 ff2 fs0 fc0 sc0 ls0 ws0">在<span class="_ _1"> </span><span class="ff1">MATLAB 2022a<span class="_ _0"> </span></span>中<span class="ff4">，</span>我们可以使用<span class="_ _1"> </span><span class="ff1">NRBO<span class="_ _0"> </span></span>来优化<span class="_ _1"> </span><span class="ff1">SVM<span class="_ _0"> </span></span>的<span class="_ _1"> </span><span class="ff1">RBF<span class="_ _0"> </span></span>函数的惩罚参数与核参数<span class="ff3">。</span>首先<span class="ff4">，</span>我</div><div class="t m0 x1 h2 y17 ff2 fs0 fc0 sc0 ls0 ws0">们需要定义适应度函数<span class="ff4">，</span>即<span class="_ _1"> </span><span class="ff1">SVM<span class="_ _0"> </span></span>的损失函数<span class="ff3">。</span>然后<span class="ff4">，</span>我们使用<span class="_ _1"> </span><span class="ff1">NRBO<span class="_ _0"> </span></span>进行迭代优化<span class="ff4">，</span>寻找最佳的参数</div><div class="t m0 x1 h2 y18 ff2 fs0 fc0 sc0 ls0 ws0">组合<span class="ff3">。</span>在每一次迭代中<span class="ff4">，<span class="ff1">NRBO<span class="_ _0"> </span></span></span>都会根据当前的参数组合计算适应度函数的值<span class="ff4">，</span>并据此调整参数<span class="ff4">，</span>以</div><div class="t m0 x1 h2 y19 ff2 fs0 fc0 sc0 ls0 ws0">使适应度函数达到最小值<span class="ff3">。</span></div><div class="t m0 x1 h2 y1a ff2 fs0 fc0 sc0 ls0 ws0">六<span class="ff3">、</span>实验结果与分析</div></div><div class="pi" data-data='{"ctm":[1.568627,0.000000,0.000000,1.568627,0.000000,0.000000]}'></div></div>