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极限学习机和自适应稀疏表示

于 2022-07-12 发布 文件大小:6.68 MB
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代码说明:

算法EA-SRC的演示在“Demo_Classification_EASRC”中给出;除了主算法之外,还给出了利用具有计算效率LOO方法(RELM-LOO)的正则化ELM的主要贡献,其中“Demo_Classification”和“Demo_Regression”中有演示;回归者包括具有计算效率的正则化ELM方法(RELM-LOO)在名为“回归者”的文件夹“/ utilities”中给出;文件夹 "/l1ls包括四个代表性稀疏重建算法;包括两个分类应用程序和一个回归应用程序。

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