Aaron E. Maxwell

TL;DR: An overview of machine learning from an applied perspective focuses on the relatively mature methods of support vector machines, single decision trees (DTs), Random Forests, boosted DTs, artificial neural networks, and k-nearest neighbours (k-NN).

TL;DR: This letter evaluates the effectiveness of a new kernel-based extreme learning machine (ELM) algorithm for a land cover classification using both multi- and hyperspectral remote-sensing data and suggests that the new algorithm is similar to, or more accurate than, SVM in terms of classification accuracy.

TL;DR: Four sample selection methods—simple random, proportional stratified random, disproportional stratified Random, and deliberative sampling—as well as three cross-validation tuning approaches—k-fold, leave-one-out, and Monte Carlo methods are investigated.

TL;DR: Support vector machines generally outperformed k-NN and the ensemble tree classifiers when only using the band means, and K-NN suffered reduced classification accuracy with high-dimensional feature spaces, suggesting that a more complex machine-learning algorithm may be more appropriate when a large number of predictor variables are used.

TL;DR: In this article, the authors compared the performance of National Agriculture Imagery Program (NAIP) orthophotography and RapidEye satellite imagery for high-resolution mapping of mining and mine reclamation within a coal surface mine in the southern coalfields of West Virginia, USA.