Daniel Peralta

TL;DR: A novel distributed partitioning methodology for prototype reduction techniques in nearest neighbor classification that enables prototype reduction algorithms to be applied over big data classification problems without significant accuracy loss and is a suitable tool to enhance the performance of the nearest neighbor classifier with big data.

TL;DR: A feature selection algorithm based on evolutionary computation that uses the MapReduce paradigm to obtain subsets of features from big datasets, improving both the classification accuracy and its runtime when dealing with big data problems.

TL;DR: A review and categorize the vast number of fingerprint matching methods proposed in the specialized literature, focusing on local minutiae-based matching algorithms, which provide good performance with an excellent trade-off between efficacy and efficiency.

TL;DR: The Cloud Computing environment rises as a natural platform to provide support to e-Learning systems and also for the implementation of data mining techniques that allow to explore the enormous data bases generated from the former process to extract the inherent knowledge.

TL;DR: This paper shows and analyzes the impact of different preprocessing techniques on the performance of three CNNs, LeNet, Network3 and DropConnect, together with their ensembles and demonstrates that data-preprocessing techniques, such as the combination of elastic deformation and rotation,together with ensembled have a high potential to further improve the state-of-the-art accuracy in MNIST classification.