1 citations · 1 across the 3 of their papers we have counts for
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Robust variable selection in the framework of classification with label noise and outliers: applications to spectroscopic data in agri-food
Andrea Cappozzo, Ludovic Duponchel, Francesca Greselin +1
Classification of high-dimensional spectroscopic data is a common task in analytical chemistry. Well-established procedures like support vector machines (SVMs) and partial least sq…
Robust variable selection for model-based learning in presence of adulteration
Andrea Cappozzo, Francesca Greselin, Thomas Brendan Murphy
The problem of identifying the most discriminating features when performing supervised learning has been extensively investigated. In particular, several methods for variable selec…
Anomaly and Novelty detection for robust semi-supervised learning
Andrea Cappozzo, Francesca Greselin, Thomas Brendan Murphy
Three important issues are often encountered in Supervised and Semi-Supervised Classification: class-memberships are unreliable for some training units (label noise), a proportion…
A robust approach to model-based classification based on trimming and constraints
Andrea Cappozzo, Francesca Greselin, Thomas Brendan Murphy
In a standard classification framework a set of trustworthy learning data are employed to build a decision rule, with the final aim of classifying unlabelled units belonging to the…