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Stacked-ELM
- 栈式ELM,堆叠多层ELM实现深度学习，比传统方法快且准确度高-Stacked ELM, stacked multi-layer ELM to realize deep learning, faster than the traditional method and high accuracy
ELM-Chinese-Brief
- 深度学习，超限学习机的中文简介，通过此文可以大概知道超限学习机的工作原理-Deep learning, learning machine overrun Chinese Introduction by this article can probably know works overrun learning machine
ELM分类器
- ELM是基于深度学习的分类器，运算速度快。 在B_data.m里导入待分类矩阵B.mat（1-n列为特征值，n列为标签）；运行B_data.m；再打开fuzzyEn_main.m并运行即可。(ELM is based on depth learning classifier, computing speed. In B_data.m imported matrix to be classified B.mat (1-n as eigenvalues, n as a label); Run B
Arithmetic
- 一个深度学习的小项目，有界面，可以跑，svm较准(A small project of deep learning, there is an interface, can run, SVM is more accurate)
H-ELM
- 可用作数据分类和拟合，深度极限学习机拥有深度学习的优势和自身计算速度快的优势(It can be used to classify and fit data. The deep extrme learning machine has the advantages of depth learning and fast computing speed.)
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 machine, ML-ELM. It is Huang Gu