Bayesian nonparametric adaptive classification with robust prior information

Francesco Denti, Andrea Cappozzo, Francesca Greselin

Risultato della ricerca: Contributo in libroContributo a convegno

Abstract

In a standard classification framework, a discriminating rule is usually built from a trustworthy set of labeled units. In this context, test observations will be automatically classified as to have arisen from one of the known groups encountered in the training set, without the possibility of detecting previously unseen classes. To overcome this limitation, an adaptive semi-parametric Bayesian classifier is intro- duced for modeling the test units, where robust knowledge is extracted from the training set and incorporated within the priors’ model specification. A successful application of the proposed approach in a real-world problem is addressed.
Lingua originaleEnglish
Titolo della pubblicazione ospiteBook of Short Papers SIS 2020
Pagine655-660
Numero di pagine6
Stato di pubblicazionePubblicato - 2020
EventoSIS 2020 - Pisa
Durata: 23 giu 202026 giu 2020

Convegno

ConvegnoSIS 2020
CittàPisa
Periodo23/6/2026/6/20

Keywords

  • Bayesian mixture model
  • Bayesian sdaptive learning
  • Stick-breaking prior
  • Supervised classification
  • Unobserved classes

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