Abstract

Three different strategies in order to re-train classifiers, when new labeled data become available, are presented in a multi-expert scenario. The first method is the use of the entire new dataset. The second one is related to the consideration that each single classifier is able to select new samples starting from those on which it performs a miss- classification. Finally, by inspecting the multi expert system behavior, a sample misclassified by an expert, is used to update that classifier only if it produces a miss-classification by the ensemble of classifiers. This paper provides a comparison of three approaches under different conditions on two state of the art classifiers (SVM and Naive Bayes) by taking into account four different combination techniques. Experiments have been performed by considering the CEDAR (handwritten digit) database. It is shown how results depend by the amount of the new training samples, as well as by the specific combination decision schema and by classifiers in the ensemble.

Autore Pugliese

• Impedovo Donato , Pirlo Giuseppe

Tutti gli autori

• BARBUZZI D.;IMPEDOVO D.;PIRLO G.

Titolo volume/Rivista

Non Disponibile

Anno di pubblicazione

2012

ISSN

Non Disponibile

ISBN

978-0-7695-4774-9

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

7

Ultimo Aggiornamento Citazioni

Non Disponibile

Settori ERC

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Codici ASJC

Non Disponibile