elm分类代码

代码说明：

说明：  预测分类，很赞 ELM在研究中被视为一类特殊的FNN，或对FNN及其反向传播算法的改进，其特点是隐含层节点的权重为随机或人为给定的，且不需要更新，学习过程仅计算输出权重 [2]? 。 传统的ELM具有单隐含层，在与其它浅层学习系统，例如单层感知机（single layer perceptron）和支持向量机（Support Vector Machine, SVM）相比较时，被认为在学习速率和泛化能力方面可能具有优势 ? 。ELM的一些改进版本通过引入自编码器构筑或堆叠隐含层获得了深度结构，能够进行表征学习 [3]? 。 ELM的应用包括计算机视觉和生物信息学，也被应用于一些地球科学、环境科学中的回归问题(Elm is regarded as a special kind of FNN in the research, or the improvement of FNN and its back propagation algorithm. Its characteristic is that the weights of hidden layer nodes are random or given artificially, and do not need to be updated. The learning process only calculates the output weights [2]. Traditional elm has a single hidden layer. Compared with other shallow learning systems, such as single layer perceptron and support vector machine, it is considered to have advantages in learning speed and generalization ability. Some improved versions of elm obtain deep structure by introducing self encoder to build or stack hidden layer, which can be used for representation learning. The application of elm includes computer vision and bioinformatics, and it is also applied to some regression problems in Earth Science and environmental science)

文件列表：

elm分类代码\ELM 分类.matlab, 1659 , 2020-05-19
elm分类代码\elmpredict.m, 1450 , 2020-05-19
elm分类代码\elmtrain.m, 1752 , 2020-05-19
elm分类代码\iris_data.mat, 363 , 2020-05-19
elm分类代码, 0 , 2020-05-19