example_data

<p>A data-set of 2000 observations were sampled independently according to the function: 
$$y_n = \dfrac{1}{1 + \exp(-x_n^T\beta + \varepsilon_n)},$$
where $x_n^T$ is a vector containing an intercept and five input features, $\beta$ is a vector containing the parameters, $(-1\;2\;1\;2\;0.5\;3)^T$, and $\varepsilon_n$ is normally distributed noise with mean 0 and variance 0.1. Furthermore, the five features were generated as $x_1 \sim \mathcal Unif(-5, 5)$, $x_2 \sim \mathcal Unif(0, 2)$, $x_3 \sim \mathcal N(2, 4)$, $x_4 \sim \mathcal Gamma(2, 4)$, and $x_5 \sim \mathcal Beta(10, 4)$, respectively.</p>

datasets

Creation, estimation, and prediction of random weight neural networks (RWNN), Schmidt et al. (1992) <doi:10.1109/ICPR.1992.201708>, including popular variants like extreme learning machines, Huang et al. (2006) <doi:10.1016/j.neucom.2005.12.126>, sparse RWNN, Zhang et al. (2019) <doi:10.1016/j.neunet.2019.01.007>, and deep RWNN, Henríquez et al. (2018) <doi:10.1109/IJCNN.2018.8489703>. It further allows for the creation of ensemble RWNNs like bagging RWNN, Sui et al. (2021) <doi:10.1109/ECCE47101.2021.9595113>, boosting RWNN, stacking RWNN, and ensemble deep RWNN, Shi et al. (2021) <doi:10.1016/j.patcog.2021.107978>.

example_data: Example data

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