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极限学习机只需要设置网络的隐层节点个数，在算法执行过程中不需要调整网络的输入权值以及隐元的偏置，并且产生唯一的最优解，因此具有学习速度快且泛化性能好的优点。该程序是用于极限学习机分类。(Extreme learning machine only need to set the number of hidden nodes of the network, the algorithm execution process does not need to adjust the input weight
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one-class extreme learning machine, 又快又好做异常检测(one-class extreme learning machine for fast and good anomaly detection)
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极限学习机是最近比较流行的一种单隐层前馈神经网络，核极限学习机是极限学习机的改进版，此文件为核极限学习机的代码实现(Code implementation of extreme learning machine)
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采用极限学习机即（ELM）进行回归和预测的MATLAB代码, 亲测可用，只需要替换自己的数据即可.(Extreme learning machine (ELM) is used for regression and prediction with MATLAB code. You need only use your own data to represent the data in the code.)
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极限学习机，能够实现数据预测(Extreme learning machine, to achieve data prediction)
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使用极限学习机进行预测，含有实际电厂数据(Using extreme learning machine to predict, which contains actual power plant data)
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《Matlab智能算法30个案例分析》极限学习机的回归拟合及分类——对比试验研究(Regression fitting and classification of extreme learning machine -- comparative experimental study)
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极限学习机（ELM）的matlab实现，对一组数据进行回归的例子(Extreme learning machine (ELM) matlab implementation, for a group of data regression examples)
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bidirectional extreme learning machine
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extreme learning machine for training and predicting
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extreme learning machine project
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robust extreme learning machine
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极限学习机(Extreme Learning Machine) ELM，是由黄广斌提出来的求解单隐层神经网络的算法(The matlab program of ELM developed by professor Huang)
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Extreme Learning Machine for soil erosion.
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极限学习机在电力系统中的应用，matlab代码，不包含数据(Application of extreme learning machine in power system)
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短期负荷预测的集成改进极端学习机方法，能有效提高预测(Short-term Photovoltaic Generation Forecasting Based on Similar Day Selection and Extreme Learning Machine)
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4下载：
kerne online sequential Extreme learning machine
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可用作数据的拟合和分类。核极限学习机采用了核函数，将数据投射到高维空间分类(It can be used for data fitting and classification. Kernel extreme learning machine uses kernel function to project data onto high-dimensional space.)
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极限学习机的代码，机器学习，可以用于分类等.(Extreme learning machine used in classification)
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黄教授的极限学习机代码，结合他的论文看，很有用，希望有帮组(Extreme Learning Machine Code, very useful)
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