Publication | Closed Access
A neural network approach to the construction of Delaunay tessellation of points in R/sup d/
14
Citations
5
References
1994
Year
Geometric LearningEngineeringMachine LearningGeometryNeural Networks (Machine Learning)Neural NetworkConvex HullComputer-aided DesignData SciencePattern RecognitionNeural Network ApproachPattern AnalysisComputational GeometryGeometry ProcessingGeometric ModelingGeometric Feature ModelingComputer ScienceNeural Networks (Computational Neuroscience)Voronoi DiagramComputational ScienceDeep Neural NetworksGeometric AlgorithmNatural SciencesDelaunay TriangulationR/sup D/Delaunay Tessellation
Since a neural network may be designed directly from either the Delaunay tessellation (DT) or its abstract dual, the Voronoi diagram, the procedure advanced here for training a dynamic feedforward neural network to generate the DT of specified points representing exemplars in multidimensional feature space, contributes toward the goal of an all-neural approach to the synthesis of neural networks. As the expected number of simplexes in the DT over n points is linear in n, the procedure is convenient for real-time implementation of pattern classifiers.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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