5 citations · 7 across the 5 of their papers we have counts for
6 papers
Directional quantile classifiers
Alessio Farcomeni, Marco Geraci, Cinzia Viroli
We introduce classifiers based on directional quantiles. We derive theoretical results for selecting optimal quantile levels given a direction, and, conversely, an optimal directio…
Classifying textual data: shallow, deep and ensemble methods
Laura Anderlucci, Lucia Guastadisegni, Cinzia Viroli
This paper focuses on a comparative evaluation of the most common and modern methods for text classification, including the recent deep learning strategies and ensemble methods. Th…
Quantile-based clustering
Christian Hennig, Cinzia Viroli, Laura Anderlucci
A new cluster analysis method, -quantiles clustering, is introduced. -quantiles clustering can be computed by a simple greedy algorithm in the style of the classical Lloyd's…
Deep Gaussian Mixture Models
Cinzia Viroli, Geoffrey J. McLachlan
Deep learning is a hierarchical inference method formed by subsequent multiple layers of learning able to more efficiently describe complex relationships. In this work, Deep Gaussi…
The Importance of Being Clustered: Uncluttering the Trends of Statistics from 1970 to 2015
Laura Anderlucci, Angela Montanari, Cinzia Viroli
In this paper we retrace the recent history of statistics by analyzing all the papers published in five prestigious statistical journals since 1970, namely: Annals of Statistics, B…
A factor mixture analysis model for multivariate binary data
Silvia Cagnone, Cinzia Viroli
The paper proposes a latent variable model for binary data coming from an unobserved heterogeneous population. The heterogeneity is taken into account by replacing the traditional…