Towards a novel probabilistic graphical model of sequential data: a solution to the problem of structure learning and an empirical evaluation

Authors:
Marco Bongini;Edmondo Trentin
Affiliations:
Dipartimento di Ingegneria dell'Informazione, Università degli Studi di Siena, Siena, Italy;Dipartimento di Ingegneria dell'Informazione, Università degli Studi di Siena, Siena, Italy
Venue:
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
Year:
2012

Citing 3
Cited 1

Scalable pseudo-likelihood estimation in hybrid random fields

Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable statistical learning: a modular Bayesian/Markov network approach

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Towards a novel probabilistic graphical model of sequential data: fundamental notions and a solution to the problem of parameter learning

ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition

Towards a novel probabilistic graphical model of sequential data: fundamental notions and a solution to the problem of parameter learning

ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper develops a maximum pseudo-likelihood algorithm for learning the structure of the dynamic extension of Hybrid Random Field introduced in the companion paper [5]. The technique turns out to be a viable method for capturing the statistical (in)dependencies among the random variables within a sequence of patterns. Complexity issues are tackled by means of adequate strategies from classic literature on probabilistic graphical models. A preliminary empirical evaluation is presented eventually.