文件列表:

elm_hidden_layer_apply.m (1001, 2017-05-02)
elm_hidden_layer_gen.m (1237, 2017-05-02)
elm_predict.m (602, 2017-05-02)
elm_train.m (1964, 2017-05-02)
leastabsdev.m (207, 2017-05-02)
plot_robust_func.m (1164, 2017-05-02)
robust_elm_l1re_train.m (5350, 2017-05-02)
robust_elm_train.m (8039, 2017-05-02)
robust_func.m (3156, 2017-05-02)
robust_linear_train.m (4011, 2017-05-02)
robust_tree_train.m (5195, 2017-05-02)
robustelm_mpg_bisquare_l2.json (321, 2017-05-02)
test_robust_elm (0, 2017-05-02)
test_robust_elm\add_noise_real.m (518, 2017-05-02)
test_robust_elm\data (0, 2017-05-02)
test_robust_elm\data\mpg (0, 2017-05-02)
test_robust_elm\data\mpg\mpg_0.1_1.mat (10174, 2017-05-02)
test_robust_elm\data\mpg\mpg_0.1_10.mat (10149, 2017-05-02)
test_robust_elm\data\mpg\mpg_0.1_2.mat (10095, 2017-05-02)
test_robust_elm\data\mpg\mpg_0.1_3.mat (10167, 2017-05-02)
test_robust_elm\data\mpg\mpg_0.1_4.mat (10147, 2017-05-02)
test_robust_elm\data\mpg\mpg_0.1_5.mat (10125, 2017-05-02)
test_robust_elm\data\mpg\mpg_0.1_6.mat (10229, 2017-05-02)
test_robust_elm\data\mpg\mpg_0.1_7.mat (10217, 2017-05-02)
test_robust_elm\data\mpg\mpg_0.1_8.mat (10231, 2017-05-02)
test_robust_elm\data\mpg\mpg_0.1_9.mat (10177, 2017-05-02)
test_robust_elm\data\mpg\mpg_0.2_1.mat (10345, 2017-05-02)
test_robust_elm\data\mpg\mpg_0.2_10.mat (10275, 2017-05-02)
test_robust_elm\data\mpg\mpg_0.2_2.mat (10288, 2017-05-02)
test_robust_elm\data\mpg\mpg_0.2_3.mat (10198, 2017-05-02)
test_robust_elm\data\mpg\mpg_0.2_4.mat (10301, 2017-05-02)
test_robust_elm\data\mpg\mpg_0.2_5.mat (10300, 2017-05-02)
test_robust_elm\data\mpg\mpg_0.2_6.mat (10326, 2017-05-02)
test_robust_elm\data\mpg\mpg_0.2_7.mat (10264, 2017-05-02)
test_robust_elm\data\mpg\mpg_0.2_8.mat (10276, 2017-05-02)
test_robust_elm\data\mpg\mpg_0.2_9.mat (10248, 2017-05-02)
test_robust_elm\data\mpg\mpg_0.3_1.mat (10345, 2017-05-02)
test_robust_elm\data\mpg\mpg_0.3_10.mat (10457, 2017-05-02)
... ...

robust-elm-irls =========================== ## Citation If you use the codes, please cite the following paper: ``` @article{Chen2016Robust, title={Robust regularized extreme learning machine for regression using iteratively reweighted least squares}, author={Chen, Kai and Lv, Qi and Lu, Yao and Dou, Yong}, journal={Neurocomputing}, volume={230C}, pages={489--501}, year={2017}, publisher={Elsevier} } ``` ## Overview Robust-elm-irls is the robust regularized Extreme Learning Machine for regression using Iteratively Reweighted Least Squares (IRLS). Robust loss function: - L1-norm loss function - Huber loss function - Bisquare loss function - Welsch loss function Regularization: - L2-norm regularization - L1-norm regularization ## Demo ``` >> addpath(genpath('.')) >> test_robust_elm_sinc.m ``` ``` >> addpath(genpath('.')) >> test_robust_elm_run.m dataset = mpg trainsize = [235 7] testsize = [157 7] loss_type = bisquare regu_type = l2 metric_type = rmse Nh_nodes = 200 c_rho-elm = 4 0.02 s (0.02 s) | ---------rls-elm----------- | rmse 8.4299 - 4.4893 | 0.04 s (0.03 s) | iter 1 | bisquare loss: 383.5101 | rmse 8.9299 - 3.0993 | 0.06 s (0.02 s) | iter 2 | bisquare loss: 185.8657 | rmse 9.4228 - 2.7330 | 0.08 s (0.02 s) | iter 3 | bisquare loss: 120.8517 | rmse 9.6767 - 2.7376 | 0.10 s (0.02 s) | iter 4 | bisquare loss: 104.***57 | rmse 9.7019 - 2.7499 | 0.12 s (0.02 s) | iter 5 | bisquare loss: 104.4186 | rmse 9.7059 - 2.7549 | 0.14 s (0.01 s) | iter 6 | bisquare loss: 104.4091 | rmse 9.7071 - 2.7574 | 0.16 s (0.02 s) | iter 7 | bisquare loss: 104.1917 | rmse 9.7075 - 2.7587 | 0.17 s (0.02 s) | iter 8 | bisquare loss: 104.0996 | rmse 9.7077 - 2.7594 | 0.19 s (0.02 s) | iter 9 | bisquare loss: 104.0486 | rmse 9.7078 - 2.75*** | 0.20 s (0.02 s) | iter 10 | bisquare loss: 104.0212 | rmse 9.7079 - 2.7600 | 0.22 s (0.02 s) | iter 11 | bisquare loss: 104.0063 | rmse 9.7079 - 2.7601 | 0.24 s (0.02 s) | iter 12 | bisquare loss: 103.9***2 | rmse 9.7079 - 2.7601 | 0.26 s (0.02 s) | iter 13 | bisquare loss: 103.9938 | rmse 9.7079 - 2.7602 | 0.28 s (0.02 s) | iter 14 | bisquare loss: 103.9915 | rmse 9.7079 - 2.7602 | 0.30 s (0.02 s) | iter 15 | bisquare loss: 103.9902 | rmse 9.7079 - 2.7602 | 0.32 s (0.02 s) | iter 16 | bisquare loss: 103.***95 | rmse 9.7079 - 2.7602 | 0.34 s (0.02 s) | iter 17 | bisquare loss: 103.***91 | rmse 9.7079 - 2.7602 | 0.35 s (0.02 s) | iter 18 | bisquare loss: 103.***89 | rmse 9.7079 - 2.7602 | 0.37 s (0.02 s) | iter 19 | bisquare loss: 103.***88 | rmse 9.7079 - 2.7602 | 0.39 s (0.02 s) | iter 20 | bisquare loss: 103.***88 | rmse 9.7079 - 2.7602 | Num of less than 1e-10: 76 TrainTime=0.3870 s | rmse (9.7079 2.7602) || ```

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