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200 10$aGraph-Based Representations in Pattern Recognition$b[electronic resource] $e13th IAPR-TC-15 International Workshop, GbRPR 2023, Vietri sul Mare, Italy, September 6?8, 2023, Proceedings /$fedited by Mario Vento, Pasquale Foggia, Donatello Conte, Vincenzo Carletti
205   $a1st ed. 2023.
210  1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2023.
215   $a1 online resource (193 pages)
225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v14121
311 08$aPrint version: Vento, Mario Graph-Based Representations in Pattern Recognition Cham : Springer,c2023 9783031427947 
320   $aIncludes bibliographical references and index.
327   $aGraph Kernels and Graph Algorithms -- Quadratic Kernel Learning for Interpolation Kernel Machine Based Graph Classification -- Minimum Spanning Set Selection in Graph Kernels -- Graph-based vs. Vector-based Classification: A Fair Comparison -- A Practical Algorithm for Max-Norm Optimal Binary Labeling of Graphs -- Efficient Entropy-based Graph Kernel -- Graph Neural Networks -- GNN-DES: A new end-to-end dynamic ensemble selection method based on multi-label graph neural network -- C2N-ABDP: Cluster-to-Node Attention-based Differentiable Pooling -- Splitting Structural and Semantic Knowledge in Graph Autoencoders for Graph Regression -- Graph Normalizing Flows to Pre-image Free Machine Learning for Regression -- Matching-Graphs for Building Classification Ensembles -- Maximal Independent Sets for Pooling in Graph Neural Networks -- Graph-based Representations and Applications -- Detecting Abnormal Communication Patterns in IoT Networks Using Graph Neural Networks -- Cell segmentation of in situ transcriptomics data using signed graph partitioning -- Graph-based representation for multi-image super-resolution -- Reducing the Computational Complexity of the Eccentricity Transform -- Graph-Based Deep Learning on the Swiss River Network.
330   $aThis book constitutes the refereed proceedings of the 13th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2023, which took place in Vietri sul Mare, Italy, in September 2023. The 16 full papers included in this book were carefully reviewed and selected from 18 submissions. They were organized in topical sections on graph kernels and graph algorithms; graph neural networks; and graph-based representations and applications.
410  0$aLecture Notes in Computer Science,$x1611-3349 ;$v14121
606   $aPattern recognition systems
606   $aComputer science$xMathematics
606   $aDiscrete mathematics
606   $aComputer graphics
606   $aAlgorithms
606   $aArtificial intelligence$xData processing
606   $aArtificial intelligence
606   $aAutomated Pattern Recognition
606   $aDiscrete Mathematics in Computer Science
606   $aComputer Graphics
606   $aAlgorithms
606   $aData Science
606   $aArtificial Intelligence
615  0$aPattern recognition systems.
615  0$aComputer science$xMathematics.
615  0$aDiscrete mathematics.
615  0$aComputer graphics.
615  0$aAlgorithms.
615  0$aArtificial intelligence$xData processing.
615  0$aArtificial intelligence.
615 14$aAutomated Pattern Recognition.
615 24$aDiscrete Mathematics in Computer Science.
615 24$aComputer Graphics.
615 24$aAlgorithms.
615 24$aData Science.
615 24$aArtificial Intelligence.
676   $a006.37
702   $aVento$b Mario$f1960-
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