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Graph-Based Representations in Pattern Recognition: 13th IAPR-TC-15 International Workshop, GbRPR 2023, Vietri sul Mare, Italy, September 68, 2023, Proceedings 1st ed. 2023 [Pehme köide]

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  • Formaat: Paperback / softback, 184 pages, kõrgus x laius: 235x155 mm, kaal: 314 g, 27 Illustrations, color; 6 Illustrations, black and white; XVI, 184 p. 33 illus., 27 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 14121
  • Ilmumisaeg: 24-Aug-2023
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031427947
  • ISBN-13: 9783031427947
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Graph-Based Representations in Pattern Recognition: 13th IAPR-TC-15 International Workshop, GbRPR 2023, Vietri sul Mare, Italy, September 68, 2023, Proceedings 1st ed. 2023Suurem pilt
  • Formaat: Paperback / softback, 184 pages, kõrgus x laius: 235x155 mm, kaal: 314 g, 27 Illustrations, color; 6 Illustrations, black and white; XVI, 184 p. 33 illus., 27 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 14121
  • Ilmumisaeg: 24-Aug-2023
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031427947
  • ISBN-13: 9783031427947
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783031427947.html
Märksõnad:
This 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.
Graph 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 Autoencodersfor 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.