4 citations · 5 across the 2 of their papers we have counts for
3 papers
stat.ML2021★ 1 cited
Learning the hypotheses space from data through a U-curve algorithm
Diego Marcondes, Adilson Simonis, Junior Barrera
This paper proposes a data-driven systematic, consistent and non-exhaustive approach to Model Selection, that is an extension of the classical agnostic PAC learning model. In this…
math.PR2019
Local Lift Dependence Scale
Diego Marcondes, Adilson Simonis
We propose a local and general dependence quantifier between two random variables and , which we call Local Lift Dependence Scale, that does not assume any form of dependenc…
stat.CO2017★ 4 cited
Feature Selection based on the Local Lift Dependence Scale
Diego Marcondes, Adilson Simonis, Junior Barrera
This paper uses a classical approach to feature selection: minimization of a cost function applied on estimated joint distributions. However, the search space in which such minimiz…