C4.5: programs for machine learning
C4.5: programs for machine learning
Organizing Large Structural Modelbases
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Algorithm for Error-Tolerant Subgraph Isomorphism Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph theory and its applications
Graph theory and its applications
An Algorithm for Subgraph Isomorphism
Journal of the ACM (JACM)
Structural Graph Matching Using the EM Algorithm and Singular Value Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Symbol Recognition by Error-Tolerant Subgraph Matching between Region Adjacency Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Algorithmics and applications of tree and graph searching
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Learning Structural Variations in Shock Trees
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Using attributed plex grammars for the generation of image and graph databases
Pattern Recognition Letters - Special issue: Graph-based representations in pattern recognition
A (Sub)Graph Isomorphism Algorithm for Matching Large Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Database Filtering Using Decision Trees
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
A graph matching based approach to fingerprint classification using directional variance
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
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Graphs are a powerful representation formalism for structural data. They are, however, very expensive from the computational point of view. In pattern recognition and intelligent information processing it is often necessary to match an unknown sample against a database of candidate patterns. In this process the size of the database is introduced as an additional factor into the overall complexity of the matching process. To reduce the influence of that factor, an approach based on machine learning techniques is proposed in this paper. Firstly, graphs are represented using feature vectors. Then, based on these vectors, a decision tree is built to index the database. At runtime the decision tree allows one to eliminate a number of graphs from the database to reduce possible matching candidates.