@InProceedings{HanaJarraya2017,
author="Hana Jarraya
and Oriol Ramos Terrades
and Josep Llados",
title="Learning structural loss parameters on graph embedding applied on symbolic graphs",
booktitle="12th IAPR International Workshop on Graphics Recognition",
year="2017",
abstract="We propose an amelioration of proposed Graph Embedding (GEM) method in previous work that takes advantages of structural pattern representation and the structured distortion. it models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector, as new signature of AG in a lower dimensional vectorial space. We focus to adapt the structured learning algorithm via 1\_slack formulation with a suitable risk function, called Graph Edit Distance (GED). It defines the dissimilarity of the ground truth and predicted graph labels. It determines by the error tolerant graph matching using bipartite graph matching algorithm. We apply Structured Support Vector Machines (SSVM) to process classification task. During our experiments, we got our results on the GREC dataset.",
optnote="DAG; 600.097; 600.121",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3073), last updated on Mon, 07 Dec 2020 14:27:55 +0100",
doi="10.1109/ICDAR.2017.268"
}