全网最全极限学习机（ELM）及其变种的开源代码分享

发布时间：2024-10-14 08:01

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分享一个小技巧，浅浅分享一下极限学习机及其变种的开源代码，需要的小伙伴下面自取呀~

极限学习机（ELM）

基本 ELM 算法
- MATLAB版
- C/C++版
- Python版
- Java版
多层/分层 ELM
- MNIST OCR 的多层 ELM
- Hierarchical ELM （分层ELM）
其他 ELM变种的相关源代码
- 3D 图形形状
- 高性能极限学习机
- 蛋白质和基因组分析
- BODIPY荧光染料的电子激发能预测
- 不平衡数据集的加权 ELM
- 双向极限学习机
- 自适应进化极限学习机
- 完全复杂的极限学习机
- 在线顺序 ELM
- 集群 ELM
Reference

基本 ELM 算法

MATLAB版

基本ELM的 MATLAB 代码（带有随机生成的隐藏节点、随机神经元），这些随机隐藏节点包括 sigmoid、RBF、傅里叶级数等。

http://www.extreme-learning-machines.org/elm_random_hidden_nodes.html

内核的 ELM 资源（用于回归和多类分类）

http://www.extreme-learning-machines.org/elm_kernel.html

OS-ELM 的源代码

http://www.extreme-learning-machines.org/source_codes/OS-ELM.zip

C/C++版

感谢意大利鲁昂大学的Vladislavs Dovgalecs对 C/C++ 版本的 ELM 的善意贡献

http://www.extreme-learning-machines.org/source_codes/elm_linear.zip
简要描述算法及其主要优点以及代码链接的博客条目
http://dovgalecs.com/blog/extreme-learning-machine-matlab-mex-implementation/

Python版

感谢 A. Akusok, K. Bjork、Y. Miche 和 A. Lendasse 对 ELM 的 Python 版本的善意贡献可以在下面

https://pypi.python.org/pypi/hpelm

感谢David Lambert对 ELM 的 Python 版本的善意贡献，可以从这个 ELM 门户网站

http://www.extreme-learning-machines.org/source_codes/Python-ELM-master.zip

简要描述算法和代码链接的博客条目

https://github.com/dclambert/Python-ELM

Java版

感谢李东为 ELM 的 Java 版本提供了帮助，可以从这个 ELM 门户网站

http://www.extreme-learning-machines.org/source_codes/ELM-Java.zip

多层/分层 ELM

MNIST OCR 的多层 ELM

L. L. C. Kasun, H. Zhou, G.-B. Huang, and C. M. Vong, “Representational Learning with Extreme Learning Machine for Big Data,” IEEE Intelligent Systems, vol. 28, no. 6, pp. 31-34, December 2013.

http://www.extreme-learning-machines.org/source_codes/MNIST-OCR-ELM.zip

Hierarchical ELM （分层ELM）

Jiexiong Tang, Chenwei Deng, and Guang-Bin Huang, “Extreme Learning Machine for Multilayer Perceptron,” (accepted by)IEEE Transactions on Neural Networks and Learning Systems, 2015.

http://www.extreme-learning-machines.org/pdf/H-ELM.pdf
http://www.extreme-learning-machines.org/source_codes/H-ELM.zip

其他 ELM变种的相关源代码

3D 图形形状

Z. Xie, K. Xu, W. Shan, L. Liu, Y. Xiong, and H. Huang, “Projective Feature Learning for 3D Shapes with Multi-View Depth Images,” Pacific Graphics, vol. 24, no. 7, 2015

http://www.kevinkaixu.net/project/mvd-elm.html

高性能极限学习机

A. Akusok, K. Bjork, Y. Miche, and A. Lendasse, “High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications,” IEEE Open Access, vol. 3, 2015

https://pypi.python.org/pypi/hpelm
https://github.com/akusok/hpelm

蛋白质和基因组分析

C. Savojardo, P. Fariselli, and R. Casadio, “BETAWARE: a machine-learning tool to detect and predict transmembrane beta barrel proteins in Prokaryotes,” Bioinformatics, Jan 13 2013. [source-codes link: BETAWARE] (for protein and genome analysis)

http://www.biocomp.unibo.it/~savojard/betawarecl/

BODIPY荧光染料的电子激发能预测

J.-N. Wang, J.-L. Jin, Y. Geng, S.-L. Sun, H.-L. Xu, Y.-H. Lu and Z.-M. Su, “An accurate and efficient method to predict the electronic excitation energies of BODIPY fluorescent dyes,” Journal of Computational Chemistry, vol. 34, no. 7, pp. 566-575, 2013 [Free Online Web Service:EEEBPre -ELM based prediction of electronic excitation energies for BODIPY dyes, which is freely accessible to public at the web site: http://202.198.129.218, has been built for prediction by the authors. This web server can return the predicted electronic excitation energy values of BODIPY dyes that are high consistent with the experimental values. The authors hope that this web server would be helpful to theoretical and experimental chemists in related research.]

http://202.198.129.218/

不平衡数据集的加权 ELM

W. Zong, G.-B. Huang, and Y. Chen, “Weighted extreme learning machine for imbalance learning,” Neurocomputing, vol. 101, pp. 229-242, 2013.

http://www.extreme-learning-machines.org/source_codes/Weighted-ELM.zip

双向极限学习机

Y. Yang, Y. Wang, and X. Yuan, “Bidirectional extreme learning machine for regression problem and its learning effectiveness,” IEEE Transactions on Neural Networks and Learning Systems, Vol. 23, pp. 1498 - 1505, 2012

http://www.extreme-learning-machines.org/source_codes/B-ELM.zip

自适应进化极限学习机

J. Cao, Z. Lin, and G.-B. Huang, “Self-adaptive evolutionary extreme learning machine,” Neural Processing Letters, vol. 36, pp. 285-305, 2012.

http://www.extreme-learning-machines.org/source_codes/SaDE-ELM.rar

完全复杂的极限学习机

M.-B. Li, G.-B. Huang, P. Saratchandran, and N. Sundararajan, “Fully Complex Extreme Learning Machine,” Neurocomputing, vol. 68, pp. 306-314, 2005.

http://www.extreme-learning-machines.org/source_codes/Compelx-ELM.zip

在线顺序 ELM

N.-Y. Liang, G.-B. Huang, P. Saratchandran, and N. Sundararajan, “A Fast and Accurate On-line Sequential Learning Algorithm for Feedforward Networks,\" IEEE Transactions on Neural Networks, vol. 17, no. 6, pp. 1411-1423, 2006

http://www.extreme-learning-machines.org/source_codes/OS-ELM.zip

集群 ELM

G. Huang, S. Song, J. N. D. Gupta, and C. Wu, “Semi-supervised and Unsupervised Extreme Learning Machines,” (in press) IEEE Transactions on Cybernetics, 2014.

http://www.extreme-learning-machines.org/source_codes/SS-US-ELM.zip

Reference

http://www.extreme-learning-machines.org/

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