搜索资源列表
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10下载:
用遗传算法进行特征选取和svm参数优化的程序。遗传算法工具箱goat已在压缩包 需要安装libsvm就可以直接运行。数据集采用UCI中的german数据集,并完成归一化操作,Genetic algorithm with feature selection and parameter optimization svm procedures. Genetic Algorithm Toolbox in goat need to install libsvm package can be run dir
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我个人收集的各类智能算法,共有20多个源代码,包括:遗传算法,蚁群算法,粒子群算法,微分进化算法,遗传神经网络算法,粒子群SVM算法,粒子群神经网络算法等混合算法-I collect all kinds of intelligent algorithms, a total of more than 20 source code, including: genetic algorithms, ant colony optimization, particle swarm optimization,
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遗传算法工具箱的应用,有典型的示范案例,有一定参考意义。-Application of genetic algorithm toolbox, a typical model case, a certain reference value.
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文件中包含某老师关于智能优化计算课程中的遗传算法部分的讲义,其中包含详细的例子及源代码。-File contains a calculation of the teachers on the intelligent optimization of the genetic algorithm curriculum part of the lecture, which includes detailed examples and source code.
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遗传算法工具箱以及用遗传算法找最优值,主函数是zuiyou.m-Genetic Algorithm Toolbox and using genetic algorithms to find optimal values, the main function is zuiyou.m
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自适应GA SVM 参数选择算法研究Param eter selection algorithm for support vector machines based
on adaptive genetic algorithm
支持向量机是一种非常有前景的学习机器, 它的回归算法已经成功地用于解决非线性函数的逼近问题. 但
是, SVM 参数的选择大多数是凭经验选取, 这种方法依赖于使用者的水平, 这样不仅不能获得最佳的函数逼近效果,
而且采用人工的方法选择 SVM 参数比较浪费
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在遗传算法中,种群只有保持一定的多样性才能进化,一般来说,在进化初期,由于种群个体差别较大,因此种群的多样性程度较强-In the genetic algorithm, the population only maintain certain diversity can evolve, generally, in the early evolution, due to the large population individual differences, so the diversity of
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关于SVM的遗传算法参数寻找最优的方法,可以参照改写其他程序-The genetic algorithm about SVM parameters to find the best method, can consult to rewrite the other programs
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利用遗传算法对支持向量机(SVM)进行分类优化-optimizate the support vector machine (SVM) classification by using the genetic algorithm
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联合运用粗糙集(RS)理论-遗传算法(GA)-支持向量机(SVM)方法研究真核生物翻译起始位点(TIS)的识别.-A classification model is built to recognize translation initiation sites (TISs) in eukaryotes by applying rough sets-genetic algorithm-support vector machine (RS-GA-SVM).
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Support Vector Machines, one of the new techniques for pattern classifi cation, have been widely used in many application areas. The kernel
parameters setting for SVM in a training process impacts on the classifi cation accuracy. Feature
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结合遗传算法和SVM分类器方法对特征基因进行筛选-Combined with genetic algorithm and SVM classifier feature gene screening
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本文主要研究 LS-SVM 算法的基本原理及基于其实现时间序列预测
的方法。针对超参数选择困难的问题,采用多级网格搜索和遗传算法的方法对
超参数的取值进行优化-This paper studies the basic principles of the LS-SVM algorithm and time series prediction based on its implementation. Difficult to select for hyper parameter, multi
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关于遗传算法的SVM的代码,有的需要自己改改,很不错的。-Code about genetic algorithm of SVM, some need to change myself, very good.
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基于SVM的数据分类预测—一种最基本遗传算法和粒子群算法对的支持向量机的参数的优化,再此基础上可以对算法进行改进-Data classification based on SVM prediction- one of the most basic genetic algorithm and particle swarm optimization (pso) algorithm, the optimization of the parameters of the support vector ma
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遗传算法和粒子群算法以及网格搜索法优化神经网络SVM的高斯核参数和惩罚参数-Optimization of Genetic Algorithm Neural Network SVM Gaussian kernel parameters and penalty parameter
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基于matlab的Iris、乳腺癌数据集的模式识别分类算法,含有 遗传算法+SVM、isodata、感知器算法、LMSE、神经网络等算法的实现代码,用于聚类效果良好,是模式识别大作业的参考资料(The pattern recognition classification algorithm based on MATLAB for Iris and breast cancer data sets contains the implementation code of genetic algorit
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利用GA遗传算法优化参数 并利用支持向量机分类或者回归计算(Use GA genetic algorithm to optimize parameters and use support vector machine for classification or regression calculation)
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