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extreme learning machine例子
run sinc_mean
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极限学习机在新环境下的应用，及其快速的算法-Extreme Learning Machine for applications in the new environment
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基于传统支持向量机(sVM)训练速度慢、参数选择难等问题，提出了基于极限学习机(ELM)的岩性识别。该算法是一种新的单隐层前馈神经网络(sLFNs)学习算法，不但可以简化参数选择过程，而且可以提高网络的训练速度。-Based on the traditional support vector machine (sVM) training is slow, difficult issues such as parameter selection, extreme learning machine
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基于极限学习机(ELM)的岩性识别。该算法是一种新的单隐层前馈神经网络(sLFNs)学习算法，不但可以简化参数选择过程，而且可以提高网络的训练速度。-Based on the traditional support vector machine (sVM) training is slow, difficult issues such as parameter selection, extreme learning machine is proposed based on (ELM) of li
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极限学习机，一种很快的学习算法，有很好的泛化性能，性能略低于SVM,但快于SVM。-Extreme learning machine, a very fast learning algorithm has good generalization of features, performance was slightly lower than SVM, but faster than the SVM.
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Extreme learning machine is used to classify seizures based on feature extraction
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extreme learning machine for feature classification
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extreme learning machine matlab code
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该算法完整地描述了极限学习机对混凝土强度预测的过程-The algorithm is a complete descr iption of extreme learning machine for concrete strength prediction process
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ELM算法Matlab实现,Extreme Learning Machine是一种快速神经网络学习方法-Extreme Learning Machine
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matlab智能算法案例之一，无导师学习神经网络-The regression and classification of Extreme Learning Machine. matlab intelligent algorithm case
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极值学习机，一种最近流行的算法。它的使用，使得计算过程大大的简化，值得交流分享。-Extreme Learning Machine, a recently popular algorithms. Its use, so that the calculation process is greatly simplified, worth sharing.
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在线极限学习机函数，包含拟合和分类功能，有详细注释-Extreme Learning Machine function includes fitting and classification, detailed notes
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极限学习机的回归拟合和分类，测试是没问题可以用的。-Extreme Learning Machine regression fit and classification, the test is no problem with.
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对极限学习机的数据的补充。This paper has demonstrated that ELM can be used
efficiently in many applications, however, two more
interesting aspects are still open: the universal approximation
capability9 of ELM and the performance of ELM in
sparse high-dim
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极限学习机（extreme learning machine）ELM是一种简单易用、有效的单隐层前馈神经网络SLFNs学习算法。2006年由南洋理工大学黄广斌副教授提出。传统的神经网络学习算法（如BP算法）需要人为设置大量的网络训练参数，并且很容易产生局部最优解。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。-Extreme Learning Machine (extreme learn
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ELM做的文字识别，有文字库，识别精度非常高-handwriting cognification by Extreme Learning Machine
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黄广斌等人提出的极限学习机理论，使用 Matlab 实现，可用于回归和分类问题!-Huangguang Bin, who proposed the theory of extreme learning machine, using Matlab to achieve, can be used for regression and classification problems!
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智能算法：神经网络，极限学习机，遗传算法，粒子群算法，蚁群算法，鱼群算法，人工免疫算法-Intelligent algorithms: neural networks, Extreme Learning Machine, genetic algorithm, particle swarm optimization, ant colony algorithm, fish swarm algorithm, artificial immune algorithm
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基于极限学习机的不平衡数据集分类，性能极好，且速度较快-Based on extreme learning machine for imbalanced data sets classification, excellent performance, and faster
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