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Extreme Learning Machine. Outline. Experimental Results ELM Weighted ELM Locally Weighted ELM Problem. Experiment. All training data are randomly chosen Targets are normalize -1 to 1 Features are normalize 0 to 1 Using RMSE criterion. Experimental results. Sinc function:

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## Extreme Learning Machine

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**Outline**• Experimental Results • ELM • Weighted ELM • Locally Weighted ELM • Problem**Experiment**• All training data are randomly chosen • Targets are normalize -1 to 1 • Features are normalize 0 to 1 • Using RMSE criterion**Experimental results**• Sinc function: • X=-10:0.05:10 • Train:351 • Test:50 • (hidden neuron, h, k)**Function:**• X=-5:0.05:5 • Train:151 • Test:50 • (hidden neuron, h, k)**Function:**• X1,x2,x3=-1:0.005:1 • Train:351 • Test:50 • (hidden neuron, h, k)**Machine CPU**• Feature:6 • Train:100 • Test:109 • (hidden neuron, h, k)**Auto Price**• Feature:15 ,1 nominal ,14 continuous • Train:80 • Test:79 • (hidden neuron, h, k)**Cancer**• Feature:32 • Train:100 • Test:94 • (hidden neuron, h, k)**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.**Locally Weighted ELM**• Find the k nearest training data to testing data**Problem**• Paper數據 • Randomly weight and bias • The output of Nearest data • (feature selection…?)

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