Now showing items 1-4 of 4
Do we need hundreds of classifiers to solve real world classification problems?
(Journal of Machine Learning Research, 2014)
We evaluate 179 classifiers arising from 17 families (discriminant analysis, Bayesian, neural networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging, stacking, random forests and other ...
HypeRvieW: an open source desktop application for hyperspectral remote-sensing data processing
(Taylor & Francis, 2016)
In this article, we present a desktop application for the analysis, reference data generation, registration, and supervised spatial-spectral classification of hyperspectral remote-sensing images through a simple and intuitive ...
Sparse matrix classification on imbalanced datasets using convolutional neural networks
This paper deals with the class imbalance problem in the context of the automatic selection of the best storage format for a sparse matrix with the aim of maximizing the performance of the sparse matrix vector multiplication ...
Dual-Window Superpixel Data Augmentation for Hyperspectral Image Classification
Deep learning (DL) has been shown to obtain superior results for classification tasks in the field of remote sensing hyperspectral imaging. Superpixel-based techniques can be applied to DL, significantly decreasing training ...