Abstract

The need for feasible inference in Probabilistic Graphical Models (PGMs) has lead to tractable models like Sum-Product Networks (SPNs). Their highly expressive power and their ability to provide exact and tractable inference make them very attractive for several real world applications, from computer vision to NLP. Recently, great attention around SPNs has focused on structure learning, leading to different algorithms being able to learn both the network and its parameters from data. Here, we enhance one of the best structure learner, LearnSPN, aiming to improve both the structural quality of the learned networks and their achieved likelihoods. Our algorithmic variations are able to learn simpler, deeper and more robust networks. These results have been obtained by exploiting some insights in the building process done by LearnSPN, by hybridizing the network adopting tree-structured models as leaves, and by blending bagging estimations into mixture creation. We prove our claims by empirically evaluating the learned SPNs on several benchmark datasets against other competitive SPN and PGM structure learners.

Autore Pugliese

• Di Mauro Nicola

Tutti gli autori

• VERGARI A.;ESPOSITO F.;DI MAURO N.

Titolo volume/Rivista

Non Disponibile

Anno di pubblicazione

2015

ISSN

1611-3349

ISBN

978-3-319-23524-0

Numero di citazioni Wos

7

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

19

Ultimo Aggiornamento Citazioni

Non Disponibile

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile