Abstract

In many textual repositories, documents are organized in a hierarchy of categories to support a thematic search by browsing topics of interests. In this paper we present a novel approach for automatic classification of documents into a hierarchy of categories that works in the transductive setting and exploits relevant example selection. While resorting to the transductive learning setting permits to classify repositories where only few examples are labelled by exploiting information potentially conveyed by unlabelled data, relevant example selection permits to tame the complexity of the task and increase the rate of learning by focusing only on informative examples. Results on real world datasets show the effectiveness of the proposed solutions

Autore Pugliese

• Ceci Michelangelo

Tutti gli autori

• CECI M.

Titolo volume/Rivista

Non Disponibile

Anno di pubblicazione

2010

ISSN

0302-9743

ISBN

978-364215250-4

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

Non Disponibile

Ultimo Aggiornamento Citazioni

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Settori ERC

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Codici ASJC

Non Disponibile