Extreme Learning Machine

1. Extreme Learning Machine

2. Outline • Experimental Results • ELM • Weighted ELM • Locally Weighted ELM • Problem

3. Experiment • All training data are randomly chosen • Targets are normalize -1 to 1 • Features are normalize 0 to 1 • Using RMSE criterion

4. Experimental results • Sinc function: • X=-10:0.05:10 • Train:351 • Test:50 • (hidden neuron, h, k)

6. Function: • X=-5:0.05:5 • Train:151 • Test:50 • (hidden neuron, h, k)

8. Function: • X1,x2,x3=-1:0.005:1 • Train:351 • Test:50 • (hidden neuron, h, k)

9. Machine CPU • Feature:6 • Train:100 • Test:109 • (hidden neuron, h, k)

10. Auto Price • Feature:15 ,1 nominal ,14 continuous • Train:80 • Test:79 • (hidden neuron, h, k)

11. Cancer • Feature:32 • Train:100 • Test:94 • (hidden neuron, h, k)

12. ELM Input layer hidden layer output layer The weights between input layer and hidden layer and the biases of neurons in the hidden layer are randomly chosen.

13. Weighted ELM

15. Ex

17. Locally Weighted ELM • Find the k nearest training data to testing data

18. Problem • Paper數據 • Randomly weight and bias • The output of Nearest data • (feature selection…?)