基于matlab的svm的留一法代码实现-AROMS-MachineLearning:机器学习的自动表示优化和模型选择框架-AROMS-Fra

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基于matlab的svm的留一法代码实现AROMS-机器学习 机器学习的自动表示优化和模型选择框架-AROMS-Framework 这是AROMS-Framework的Matlab源代码的发布。 这是我在德国杜伊斯堡-埃森大学的智能系统小组(Intelligente Systeme)的博士项目的主要贡献。 我的博士学位论文题目为“机器学习的自动表示优化和模型选择框架”在线发表: 英博士Dipl.-Inf。 FabianBürger,2016年7月4日 与我联系的一种方法是:fabuerger | at | gmail | dot | com ==应用领域和方法== AROMS框架的应用领域是针对监督分类问题的数据处理管道的优化。 管道非常适合每项学习任务,由四个连续处理数据的元素组成: 特征选择元素选择有用的特征子集 功能预处理元素应用数据预处理方法,例如重新缩放,L2归一化或预白化 特征变换元素从流形学习和表示学习领域应用合适的特征变换,例如主成分分析(PCA),自动编码器或LLE(局部线性嵌入) 分类器元素包含分类器,并提供了多种选择,例如内核支持向量机(SVM),随机森林或人工神

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