We present ESSYS* Sharing #UC, an audiovisual installation artwork that reflects upon the emotional context related to the university and the city of Coimbra, based on the data shared about them on Twitter.
Decision diagrams for classification have some notable advantages over decision trees, as their internal connections can be determined at training time and their width is not bound to grow exponentially with their depth.
We also compared our results with a control method based on the standard machine learning pipeline (pre-processing, feature extraction, classifier training, and post-processing), where the control marginally outperformed our approach by validating 35% of the patients.
On a different direction, using the second index, the strong target members should characterize relevant consumers of information in the network, which may include fake news' regular collectors.
Social and Information Networks 68R10, 90B18
A descriptive approach for automatic generation of visual blends is presented.
In this case, the source/target domains are represented in the form of subspaces, which are treated as points on the Grassmann manifold.
In many datasets, the samples are related by a known image transformation, such as rotation, or a repeatable non-rigid deformation.
Interestingly, for linear regression our formulation is equivalent to a correlation filter, used by some of the fastest competitive trackers.
In the past few years there has been a growing interest on geometric frameworks to learn supervised classification models on Riemannian manifolds [31, 27].