Abstract: Graphs are a powerful representation formalism for structural data. They are, however, very expensive from the computational point of view.
Decision trees for filtering large databases of graphs | Semantic ...
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An approach based on machine learning techniques is proposed to reduce the influence of the size of the database as an additional factor into the overall ...
Jun 29, 2007 · Firstly, graphs are represented using feature vectors. Then, based on these vectors, a decision tree is built to index the database. At runtime ...
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Jun 1, 2007 · Firstly, graphs are represented using feature vectors. Then, based on these vectors, a decision tree is built to index the database. At runtime ...
Dec 5, 2022 · Irniger, Christoph; Bunke, Horst (2007). Decision trees for filtering large databases of graphs. International journal of intelligent ...
The decision tree allows at runtime to eliminate a number of graphs from the database as possible matching candidates. Experimental results are reported ...
Graphs are represented using feature vectors. Based on these vectors a decision tree is built to index the database. The decision tree allows at runtime to ...
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Nov 6, 2024 · Decision trees are a popular machine learning algorithm that can be used for both regression and classification tasks.
Graphs are represented using feature vectors. Based on these vectors a decision tree is built to index the database. The decision tree allows at runtime to ...
Abstract. In structural pattern recognition it is often required to match an unknown sample against a database of candidate patterns in order.