Consensus clustering has emerged as a powerful technique for obtaining better clustering results, where a set of data partitions (ensemble) are generated, which are then combined to obtain a consolidated solution (consensus ...

Machine learning techniques have been extensively researched in the last years, mainly due to their effectiveness when dealing with recognition or classification applications. Typically, one can comprehend using a Machine ...

Convolutional Neural Networks have been widely employed in a diverse range of computer vision-based applications, including image classification, object recognition, and object segmentation. Nevertheless, one weakness of ...

Ensembling machine-learned models has shown to be a useful technique for improving the effectiveness of tasks such as classification, ad-hoc retrieval, and recommendation. Stacking, for instance, learns to weight and combine ...

"Clustering methods are the most used algorithms for unsupervised learning. However, there is no unique optimal approach for all datasets since different clustering algorithms produce different partitions. To overcome this ...

In this paper, we report on the synchronization of a pacemaker neuronal ensemble constituted of an AB neuron electrically coupled to two PD neurons. By the virtue of this electrical coupling, they can fire synchronous ...