Using 5G networks for flying vehicles is an opportunity to provide reliable connectivity while reducing cost and requirements on size, weight and power consumption. Network slicing is one feature which is particularly of interest. It enables a reliable aerial vehicle control slice independent from payload communication, such as video streaming to the ground. For the

No abstract

Support Vector Machine (SVM) is an important classification method used in a many areas. The training of SVM is almost O(n 2 ) in time and space. Some methods to reduce the training complexity have been proposed in last years. Data selection methods for SVM select most important examples from training data sets to improve its training time.This paper introduces a novel data reduction method that works detecting clusters and then selects some examples from them. Different from other state of the art algorithms, the novel method uses a decision tree to form partitions that are treated as clusters, and then executes a guided random selection of examples. The clusters discovered by a decision tree can be linearly separable, taking advantage of the Eidelheit separation theorem, it is possible to reduce the size of training sets by carefully selecting examples from training sets.The novel method was compared with LibSVM using public available data sets, experiments demonstrate an important reduction of the size of training sets whereas showing only a slight decreasing in the accuracy of classifier.

No abstract

No abstract