Linear support vector machines (SVM) are useful for classifying large-scale sparse data. Problems with sparse features are common in applications such as document classification a...
Several solutions have been proposed to exploit the availability of heterogeneous sources of biomolecular data for gene function prediction, but few attention has been dedicated t...
We classify 3D aerial LiDAR scattered height data into buildings, trees, roads, and grass using the Support Vector Machine (SVM) algorithm. To do so we use five features: height, ...
Suresh K. Lodha, Edward J. Kreps, David P. Helmbol...