3 papers
cs.LG2020
Probabilistic Diagnostic Tests for Degradation Problems in Supervised Learning
Gustavo A. Valencia-Zapata, Carolina Gonzalez-Canas, Michael G. Zentner +2
Several studies point out different causes of performance degradation in supervised machine learning. Problems such as class imbalance, overlapping, small-disjuncts, noisy labels,…
cs.LG2017
A Statistical Approach to Increase Classification Accuracy in Supervised Learning Algorithms
Gustavo A Valencia-Zapata, Daniel Mejia, Gerhard Klimeck +2
Probabilistic mixture models have been widely used for different machine learning and pattern recognition tasks such as clustering, dimensionality reduction, and classification. In…
cond-mat.mtrl-sci2017
Grain Boundary Resistance in Copper Interconnects from an Atomistic Model to a Neural Network
Daniel Valencia, Evan Wilson, Zhengping Jiang +3
Orientation effects on the resistivity of copper grain boundaries are studied systematically with two different atomistic tight binding methods. A methodology is developed to model…