Journal ArticleDOI
Approximate graph edit distance computation by means of bipartite graph matching
Kaspar Riesen,Horst Bunke +1 more
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TLDR
A novel algorithm is introduced which allows us to approximately, or suboptimally, compute edit distance in a substantially faster way and is emprically verified that the accuracy of the suboptimal distance remains sufficiently accurate for various pattern recognition applications.About:
This article is published in Image and Vision Computing.The article was published on 2009-06-01. It has received 654 citations till now. The article focuses on the topics: Graph operations & Line graph.read more
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An Overview of Distance and Similarity Functions for Structured Data
TL;DR: The notions of distance and similarity play a key role in many machine learning approaches, and artificial intelligence (AI) in general, since they can serve as an organizing principle by which individuals classify objects, form concepts and make generalizations as mentioned in this paper.
Proceedings ArticleDOI
Handwritten word spotting by inexact matching of grapheme graphs
TL;DR: This paper presents a graph-based word spotting for handwritten documents using a structural representation suitable to be robust to the inherent deformations of handwriting, comparable to statistical ones in terms of time and memory requirements.
Proceedings ArticleDOI
A Machine Learning Approach to SPARQL Query Performance Prediction
Rakebul Hasan,Fabien Gandon +1 more
TL;DR: This paper uses machine learning techniques to learn SParQL query performance from previously executed queries, and shows how to model SPARQL queries as feature vectors, and uses k-nearest neighbors regression and Support Vector Machine with the nu-SVR kernel to accurately predict SPARql query execution time.
Proceedings ArticleDOI
Learning Graph Distances with Message Passing Neural Networks
TL;DR: This paper proposes an efficient graph distance based on the emerging field of geometric deep learning that employs a message passing neural network to capture the graph structure and learns a metric with a siamese network approach.
Proceedings ArticleDOI
A Coarse-to-Fine Word Spotting Approach for Historical Handwritten Documents Based on Graph Embedding and Graph Edit Distance
TL;DR: A coarse-to-fine handwritten word spotting approach based on graph representation that comprises both the topological and morphological signatures of the handwriting achieves a compromise between efficiency and accuracy.
References
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Journal ArticleDOI
The Protein Data Bank
Helen M. Berman,John D. Westbrook,Zukang Feng,Gary L. Gilliland,Talapady N. Bhat,Helge Weissig,Ilya N. Shindyalov,Philip E. Bourne +7 more
TL;DR: The goals of the PDB are described, the systems in place for data deposition and access, how to obtain further information and plans for the future development of the resource are described.
Journal ArticleDOI
The Hungarian method for the assignment problem
TL;DR: This paper has always been one of my favorite children, combining as it does elements of the duality of linear programming and combinatorial tools from graph theory, and it may be of some interest to tell the story of its origin this article.
Journal ArticleDOI
A Formal Basis for the Heuristic Determination of Minimum Cost Paths
TL;DR: How heuristic information from the problem domain can be incorporated into a formal mathematical theory of graph searching is described and an optimality property of a class of search strategies is demonstrated.
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