Classification of radar returns from the ionosphere using neural networks

By V. Sigillito, S. Wing, L. V. Hutton, K. Baker. 1989

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning (2018, background): “• Ionosphere dataset (Sigillito et al., 1989) is to predict whether it is a “good” radar return or not – “good” if the features in xn show evidence of some structures in the ionosphere.”
Slice sampling covariance hyperparameters of latent Gaussian models (2010, methodology): “We applied these methods to the Ionosphere dataset [16], using 200 training data and 34 dimensions.”
A survey of kernel and spectral methods for clustering (2008, methodology): “[17] [21] [24]) have been applied to standard benchmarks such as Ionosphere [25], Breast Cancer [26] and Iris [27].”
Ranking and combining multiple predictors without labeled data (2013, background): “Ranking and combining multiple predictors without labeled data”
Variable Selection via A Combination of the L0 and L1 Penalties (2007, background): ““Bad” returns are those that do not and their signals pass through the ionosphere (Sigillito et al. 1989).”