登录
首页 » matlab » ELM

ELM

于 2018-03-14 发布 文件大小:3947KB
0 381
下载积分: 1 下载次数: 11

代码说明:

  一种神经网络算法:极限学习机(ELM),包括分类和回归,仿真验证无误,适合初学者练习(A data mining algorithm: limit learning machine (ELM), including classification and regression, simulation verification is unmistakable, suitable for beginners to practice)

文件列表:

ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\diabetes分类\BP(diabetes)\BP_diabetes.m, 2265 , 2013-10-03
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\diabetes分类\BP(diabetes)\BP_sinc.asv, 2298 , 2013-10-03
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\diabetes分类\BP(diabetes)\diabetes2.dt, 46403 , 2004-03-18
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\diabetes分类\BP(diabetes)\diabetes2_data.m, 1161 , 2004-04-17
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\diabetes分类\BP(diabetes)\diabetes_test, 19200 , 2013-10-03
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\diabetes分类\BP(diabetes)\diabetes_train, 57600 , 2013-10-03
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\diabetes分类\ELM(diabetes)\diabetes2.dt, 46403 , 2004-03-18
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\diabetes分类\ELM(diabetes)\diabetes2_data.m, 1161 , 2004-04-17
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\diabetes分类\ELM(diabetes)\diabetes_test, 19200 , 2013-10-03
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\diabetes分类\ELM(diabetes)\diabetes_train, 57600 , 2013-10-03
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\diabetes分类\ELM(diabetes)\ELM.asv, 9389 , 2013-10-03
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\diabetes分类\ELM(diabetes)\ELM.m, 9390 , 2013-10-03
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\diabetes分类\ELM(diabetes)\ELM_diabetes.m, 874 , 2013-10-03
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\diabetes分类\ELM.m, 9385 , 2013-08-31
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\diabetes分类\elm_predict.m, 3763 , 2004-05-10
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\diabetes分类\elm_train.m, 5645 , 2004-05-10
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\diabetes分类\SVM(diabetes)\diabetes2.dt, 46403 , 2004-03-18
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\diabetes分类\SVM(diabetes)\diabetes2_data.m, 1161 , 2004-04-17
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\diabetes分类\SVM(diabetes)\diabetes_test, 19200 , 2013-10-03
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\diabetes分类\SVM(diabetes)\diabetes_train, 57600 , 2013-10-03
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\diabetes分类\SVM(diabetes)\SVM_diabetes.asv, 2224 , 2013-10-03
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\diabetes分类\SVM(diabetes)\SVM_diabetes.m, 2250 , 2013-10-03
ELM\ELMѧϰ\Basic ELM(for ELM with random hidden nodes)\elm.pdf, 460493 , 2013-10-03
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\sinc回归\BP(SINC)\BP_sinc.m, 1481 , 2013-09-14
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\sinc回归\BP(SINC)\sinc.m, 706 , 2013-09-14
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\sinc回归\BP(SINC)\sinc_mean.asv, 1463 , 2013-09-05
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\sinc回归\BP(SINC)\sinc_test, 687 , 2013-10-21
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\sinc回归\BP(SINC)\sinc_train, 2280 , 2013-10-21
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\sinc回归\ELM(SINC)\ELM.m, 7862 , 2013-09-06
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\sinc回归\ELM(SINC)\ELM_sinc.m, 780 , 2013-10-05
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\sinc回归\ELM(SINC)\sinc.m, 706 , 2013-09-14
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\sinc回归\ELM(SINC)\sinc_mean.asv, 786 , 2013-09-05
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\sinc回归\ELM(SINC)\sinc_test, 682 , 2014-05-03
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\sinc回归\ELM(SINC)\sinc_train, 2290 , 2014-05-03
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\sinc回归\ELM.m, 9385 , 2013-08-31
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\sinc回归\elm_predict.m, 3763 , 2004-05-10
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\sinc回归\elm_train.m, 5645 , 2004-05-10
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\sinc回归\SVR(SINC)\sinc.m, 706 , 2013-09-14
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\sinc回归\SVR(SINC)\sinc_mean.asv, 1463 , 2013-09-05
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\sinc回归\SVR(SINC)\sinc_test, 681 , 2013-09-14
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\sinc回归\SVR(SINC)\sinc_train, 2302 , 2013-09-14
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\sinc回归\SVR(SINC)\SVR_sinc.asv, 2185 , 2013-09-14
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\sinc回归\SVR(SINC)\SVR_sinc.m, 2186 , 2013-09-14
ELM\ELMѧϰ\B_ELM\B-ELM.zip, 1341073 , 2013-10-05
ELM\ELMѧϰ\C_ELM\Compelx-ELM.zip, 377581 , 2013-10-05
ELM\ELM学习\ELM_kernel\diabetes分类\ELM_kernel(diabetes)\diabetes2.dt, 46403 , 2004-03-18
ELM\ELM学习\ELM_kernel\diabetes分类\ELM_kernel(diabetes)\diabetes2_data.m, 1161 , 2004-04-17
ELM\ELM学习\ELM_kernel\diabetes分类\ELM_kernel(diabetes)\diabetes_test, 19200 , 2013-10-05
ELM\ELM学习\ELM_kernel\diabetes分类\ELM_kernel(diabetes)\diabetes_train, 57600 , 2013-10-05
ELM\ELM学习\ELM_kernel\diabetes分类\ELM_kernel(diabetes)\elm_kernel.m, 7861 , 2013-10-05
ELM\ELM学习\ELM_kernel\diabetes分类\ELM_kernel(diabetes)\ELM_kernel_diabetes.m, 889 , 2013-10-05
ELM\ELM学习\ELM_kernel\sinc回归\ELM_kernel(SINC)\elm_kernel.m, 7861 , 2013-10-05
ELM\ELM学习\ELM_kernel\sinc回归\ELM_kernel(SINC)\ELM_kernel_sinc.m, 795 , 2013-10-05
ELM\ELM学习\ELM_kernel\sinc回归\ELM_kernel(SINC)\sinc.m, 706 , 2013-09-14
ELM\ELM学习\ELM_kernel\sinc回归\ELM_kernel(SINC)\sinc_mean.asv, 786 , 2013-09-05
ELM\ELM学习\ELM_kernel\sinc回归\ELM_kernel(SINC)\sinc_test, 688 , 2013-10-05
ELM\ELM学习\ELM_kernel\sinc回归\ELM_kernel(SINC)\sinc_train, 2287 , 2013-10-05
ELM\ELMѧϰ\OS_ELM\OS-ELM\HardlimActFun.m, 209 , 2006-06-06
ELM\ELMѧϰ\OS_ELM\OS-ELM\OSELM.m, 7690 , 2006-06-06
ELM\ELMѧϰ\OS_ELM\OS-ELM\OSELM_VARY.m, 7778 , 2006-06-06
ELM\ELMѧϰ\OS_ELM\OS-ELM\RBFun.m, 287 , 2006-06-06
ELM\ELMѧϰ\OS_ELM\OS-ELM\SigActFun.m, 211 , 2006-06-06
ELM\ELMѧϰ\OS_ELM\OS-ELM\SinActFun.m, 199 , 2006-06-06
ELM\ELMѧϰ\OS_ELM\oselm.pdf, 666139 , 2013-10-16
ELM\ELMѧϰ\SaDE_ELM\SaDE-ELM.rar, 4877 , 2013-10-05
ELM\ELMѧϰ\Weighted_ELM\Weighted-ELM.zip, 6993 , 2013-10-05
ELM\ELM学习\测试比较图.doc, 167936 , 2013-10-05
ELM\分类\ELMfenglei.m, 1741 , 2013-08-26
ELM\分类\elmpredict.m, 1454 , 2010-11-07
ELM\分类\elmtrain.m, 1752 , 2010-11-07
ELM\分类\iris.mat, 1059 , 2009-11-14
ELM\回归\chejing.mat, 213954 , 2014-07-04
ELM\回归\elm\chejing.mat, 213954 , 2014-07-04
ELM\回归\elm\ELM.m, 2298 , 2014-07-04
ELM\回归\elm\elmpredict.m, 1454 , 2010-11-07
ELM\回归\elm\elmtrain.m, 1752 , 2010-11-07
ELM\回归\ELMhuigui.m, 3444 , 2014-07-04
ELM\回归\elmpredict.m, 1454 , 2010-11-07
ELM\回归\elmtrain.m, 1752 , 2010-11-07
ELM\回归\main.asv, 3194 , 2013-08-26
ELM\回归\psoelm\chejing.mat, 213954 , 2014-07-04
ELM\回归\psoelm\elmpredict.m, 1454 , 2010-11-07
ELM\回归\psoelm\elmtrain.m, 3605 , 2014-07-04
ELM\回归\psoelm\fun.m, 1181 , 2014-07-04
ELM\回归\psoelm\PSOELM.m, 2391 , 2014-07-04
ELM\回归\psoelm.zip, 428805 , 2014-07-04
ELM\回归\spectra_data.mat, 171497 , 2010-10-14
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\diabetes分类\BP(diabetes), 0 , 2018-03-14
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\diabetes分类\ELM(diabetes), 0 , 2018-03-14
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\diabetes分类\SVM(diabetes), 0 , 2018-03-14
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\sinc回归\BP(SINC), 0 , 2018-03-14
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\sinc回归\ELM(SINC), 0 , 2018-03-14
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\sinc回归\SVR(SINC), 0 , 2018-03-14
ELM\ELM学习\ELM_kernel\diabetes分类\ELM_kernel(diabetes), 0 , 2018-03-14
ELM\ELM学习\ELM_kernel\sinc回归\ELM_kernel(SINC), 0 , 2018-03-14
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\diabetes分类, 0 , 2018-03-14
ELM\ELM学习\Basic ELM(for ELM with random hidden nodes)\sinc回归, 0 , 2018-03-14
ELM\ELM学习\ELM_kernel\diabetes分类, 0 , 2018-03-14
ELM\ELM学习\ELM_kernel\sinc回归, 0 , 2018-03-14
ELM\ELMѧϰ\OS_ELM\OS-ELM, 0 , 2018-03-14

下载说明:请别用迅雷下载,失败请重下,重下不扣分!

发表评论

0 个回复

  • 频繁项集算法
    频繁项集挖掘算法,能在大量局部特征中发现频繁空间配置,这些空间配置可以作为词语,加入到特征包中进行分类,实现图像分类。
    2022-03-23 21:38:47下载
    积分:1
  • kaggle叶子分类
    利用一维卷积神经网络将叶子进行分类,里面包含的有数据(Classification of leaves using one dimensional convolution neural network)
    2018-07-12 20:41:43下载
    积分:1
  • 用python 做的网络爬虫
    说明:  用python 做的网络数据爬虫,爬取淘宝数据,并分析。(Use Python to do the network data crawler, crawl the Taobao data, and analyze.)
    2020-02-25 18:34:31下载
    积分:1
  • KNN01
    说明:  用来作分类识别的KNN算法,非常好用非常好用(A very useful nearest neighbor algorithm)
    2020-08-03 19:28:41下载
    积分:1
  • boxcox
    说明:  boxcox函数的python实现,引用该函数可将偏态分布调整为正态分布(Python implementation of box Cox function)
    2020-06-17 09:40:01下载
    积分:1
  • kacmp
    google搜索的核心技术,VC++源码实现,包含测试数据库()
    2017-12-03 12:35:44下载
    积分:1
  • 2555333
    牛顿插值法,选择插值节点文件,有点小麻烦()
    2018-03-15 20:34:53下载
    积分:1
  • hierarchical-clustering
    这段代码是层次聚类的Matlab实现代码( This code is hierarchical clustering Matlab implementation code)
    2016-10-16 10:43:09下载
    积分:1
  • test_lstm
    说明:  简单的LSTM进行预测,附带数据集方便测试(simple test of LSTM is used for prediction , and related datasets is attached in the file.)
    2020-08-30 16:28:10下载
    积分:1
  • matlab 实现了kmeans算法
    用matlab 实现了kmeans算法还附有评价指标计算(Matlab to achieve kmeans algorithm also attached to the evaluation index calculation)
    2020-06-19 04:40:01下载
    积分:1
  • 696516资源总数
  • 106914会员总数
  • 0今日下载