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ELM-java
- 极限学习机(ELM)在java下的实现算法，此为eclipse项目，已经添加好可用的jar包，调整文件路径后可以直接运行,(PS.极限学习机算法为elm官网所提供)(Extreme learning machine (ELM) in Java implementation algorithm, this is the eclipse project, has added a good jar package, adjust the fi
ELM
- 新加坡南洋理工黄教授编写的极限学习机的算法实现(the implementation of extreme learning machine)
Compelx-ELM
- 复数极限学习机，通过复数处理，将极限学习机扩展到复数领域，效果很好(Complex limit learning machine, through the complex processing, the limit learning machine extended to the complex domain, the effect is very good)
由Java编写的极限学习机毕业设计源代码
- 基于Java的极限学习机源代码，可用于毕业设计。(Java based limit learning machine source code.)
ELM
- 用于使用极限学习机算法进行分类问题,采用MATLAB编写，适合论文写作(Classification algorithms are used for the use of the extreme learning machine algorithm)
OS-ELM在线极限学习机
- 此代码是OS-ELM在线极限学习机，内含训练集和测试集。(This code is the OS-ELM online extreme learning machine, containing training set and test set.)
elm
- 极限学习机，分类和回归，有程序，数据和相关案例(ultimate learning machine classification and regression, procedures, data, and examples.)
ELM_MatlabClass
- 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 n
elm_kernel
- 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)
elm
- 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)
改编ELMmatlab版code
- 这个算法是用来建立极限学习机网络的12345678(This algorithm is used to set up the limit learning machine network12345678)
极限学习做特征选择思路1
- 极限学习机是一个快速的但因曾神经网络学习算法(Extreme learning is a fast learning method)
基于极限学习G-score
- G-score是一个特征排序的准则，极限学习机结合G-score是一种filter+wrapper的混合特征选择算法(G-score is a criterion of feature sorting. Limit learning machine combined with G-score is a hybrid feature selection algorithm of filter+wrapper)
07 极限学习机（Extreme Learning Machine, ELM）
- ELM算法指出，其实隐层的权值矩阵W和偏置b其实是没有必要调整的，在学习算法开始时，任意随机给定W和b的值，利用其计算出H（隐层节点的输出），并令其保持不变，需要确定的参数就只有β了。这是一个比较重要的理论基础。(The ELM algorithm is pointed out, in fact, hidden layer weights matrix W and B is not necessary to adjust the bia
极限学习机
- 极限学习机分类器，训练函数与预测函数，以及数据实例(Extreme Machine Classifiers, Training and Prediction Functions, and Data Instances)
基于极限学习机的预测
- 针对非线性预测问题，建立极限学习机的预测模型，将数据样本分为训练样本和测试样本，并采用误差指标进行评价。(Aiming at the problem of non-linear prediction, the prediction model of extreme learning machine is established. The data samples are divided into training samples and
基于极限学习机ELM的数据分类
- 针对数据分类问题，提出了基于极限学习机的分类方法，将数据样本分为训练样本和测试样本，并采用准确率指标进行评价。(Aiming at the problem of data classification, a classification method based on extreme learning machine is proposed. The data samples are divided into training samp
爬山法-遗传算法-极限学习机
- 爬山改进遗传算法，提供更快的收敛速度，并用于优化极限学习机权值(Mountain climbing improved genetic algorithm to provide faster convergence speed and to optimize the weight of extreme learning machine)
核极限学习机
- 核极限学习机程序，可以直接调用，满足分类要求。(Extreme Learning Machine Program)
深度（多层）极限学习机的python实现
- 深度极限学习机也叫多层极限学习机，ML-ELM。是黄广斌等人在极限学习机ELM基础上，将其拓展为深度学习的一种模式识别方法，原文文章：Representational learning with extreme learning machine for big data。(The deep extreme learning machine is also called the multi-layer extreme learning m