Paper Info
Title
DBN-based structural learning and optimisation for automated handwritten character recognition
Abstract
Pattern recognition using Dynamic Bayesian Networks (DBNs) is currently a growing area of study. The classification performance greatly relies on the choice of a DBN model that will best describe the dependencies in each class of data. In this paper, we present DBN models trained for the classification of handwritten digit. Two approaches to improve the suitability of the models are presented. One uses a fixed DBN structure, and is based on an Evolutionary Algorithm optimisation of the selection and of the layout of the observations for each class of data. The second approach is about learning part of the structure of the models from the training set of each class. Parameter learning is then performed for each DBN. Classification results are presented for the described models, and compared with previously published results. Both approaches were found to improve the recognition rate compared to previous results.
Year
DOI
Venue
2012
10.1016/j.patrec.2011.12.010
Pattern Recognition Letters
Keywords
Field
DocType
handwritten digit,evolutionary algorithm optimisation,automated handwritten character recognition,pattern recognition,dbn model,recognition rate,dbn-based structural learning,parameter learning,fixed dbn structure,classification result,dynamic bayesian networks,classification performance,dynamic bayesian network,supervised learning,evolutionary algorithm
Training set,Pattern recognition,Evolutionary algorithm,Character recognition,Computer science,Structural learning,Structure learning,Parameter learning,Supervised learning,Artificial intelligence,Machine learning,Dynamic Bayesian network
Journal
Volume
Issue
ISSN
33
6
0167-8655
Citations 
PageRank 
References 
4
0.46
13
Authors
2
Name
Order
Citations
PageRank
Olivier Pauplin1302.74
Jianmin Jiang298581.39