Table 4 Characteristics of artificial datasets for regression used for comparisons
From: Extreme learning machine versus classical feedforward network
Dataset | No of samples | No of features | Min. value of target variable | Max. value of target variable |
|---|---|---|---|---|
Linear function | 2000 | 1–1000 | 0 | 0 |
Second degree polynomial of multiple variables | 2000 | 1–1000 | 0 | 0 |
Sine and cosine | 2000 | 1 | −1 | 1 |
SinC | 2000 | 1 | −0.2 | 1 |
Friedman spline I | 2000 | 5–1000 | \(\sim\)0 | \(\sim\)30 |
Friedman spline II | 2000 | 4 | \(\sim\)0 | \(\sim\)1760 |
Friedman spline III | 2000 | 4 | \(\sim\)0 | \(\sim\)1760 |