搜索资源列表
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遗传算法的开发包,外网上好不容易下下来的,功能很齐全-genetic algorithm development kits, external hard-line down under the feature is complete
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模拟退火程序:这部分代码是模式识别中结合模拟退火法的特征提取，大家可以对其进行简单的改进会获得更好的效果，比如改变温度表，加上“记忆体”，结合遗传算法等
-simulated annealing procedures : This code is part of pattern recognition in combination with simulated annealing method of feature extraction, you can perform simple im
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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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genetic algorithm combine Ant colony optimization for feature selection,genetic algorithm combine Ant colony optimization for feature selection
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自动提取和分类脑电EEG信号P300的特征的遗传算法-A Genetic Algorithm for Automatic Feature Extraction
in P300 Detection
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基因算法实现的特征提取，实现平台是matlab-feature selection with genetic algorithm
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本代码是基于面部感情特征提取的一种遗传算法，-The code is based on the facial feature extraction of the feelings of a genetic algorithm,
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An automatic facial feature extraction method is
presented in this paper. The method is based on the edge
density distribution of the image. In the preprocessing stage a
face is approximated to an ellipse, and genetic algorithm is
applied
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SVM方法的基本思想是：定义最优线性超平面，并把寻找最优线性超平面的算法归结为求解一个凸规划问题。进而基于Mercer核展开定理，通过非线性映射φ，把样本空间映射到一个高维乃至于无穷维的特征空间（Hilbert空间），使在特征空间中可以应用线性学习机的方法解决样本空间中的高度非线性分类和回归等问题。svm 程序，即支持向量机的代码。-The basic idea of SVM method are: the definition of the optimal linear hyperplane,
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paper about Feature selection and parameter optimization for support vector machines:
A new approach based on genetic algorithm with feature chromosomes
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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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Genetic algorithm feature selection by using SVM
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Genetic algorithm feature selection by using Logistic Regression
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基于遗传算法寻优初始权值的BP神经网络建模-BP neural network model based on genetic algorithm optimization initial weights
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Feature Selection Using Genetic Algorithm
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feature selection with genetic algorithm
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matlab平台下的一种遗传算法，可用于波段选择或者波段选择(A Genetic Algorithm-Based Feature Selection)
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Binary GA selection method
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遗传算法提供了求解非线性规划的通用框架，它不依赖于问题的具体领域。遗传算法的优点是将问题参数编码成染色体后进行优化， 而不针对参数本身， 从而不受函数约束条件的限制； 搜索过程从问题解的一个集合开始， 而不是单个个体， 具有隐含并行搜索特性， 可大大减少陷入局部最小的可能性。而且优化计算时算法不依赖于梯度信息，且不要求目标函数连续及可导，使其适于求解传统搜索方法难以解决的大规模、非线性组合优化问题。(Genetic algorithm provides a general framework f
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用二进制遗传算法做特征选择，此算法效率高，选择的特征数目少。(The binary genetic algorithm is used for feature selection, which has high efficiency and few features.)
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