Andres J. Anaya-Isaza

TL;DR: In this paper, an overview of current deep learning methods, starting from the most straightforward concept but accompanied by the mathematical models that are behind the functionality of this type of intelligence, is presented.

TL;DR: In this paper, different deep learning (DL) architectures were compared in terms of breast lesion segmentation, lesion type classification, and degree of suspicion of malignancy tests.

TL;DR: This work presents a new interactive data visualization approach based on mixture of the outcomes of dimensionality reduction (DR) methods, whose weighting factors are defined by the user through a visual and intuitive interface.

TL;DR: A data set concerning electric-power consumption-related features registered in seven main municipalities of Nariño, Colombia, from December 2010 to May 2016 is introduced, aimed at providing researchers a suitable input for designing and assessing the performance of forecasting, modelling, simulation and optimization approaches applied to electric power consumption prediction and characterization problems.

TL;DR: This paper presents the development of a unified view of spectral clustering and unsupervised dimensionality reduction approaches within a generalized kernel framework in terms of a high-dimensional representation of the input data matrix incorporated into a least-squares support vector machine to yield a generalized optimization problem.