Graph-Based Representations in Pattern Recognition: 13th IAPR-TC-15 International Workshop, GbRPR 2023,...

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Mario Vento Pasquale Foggia

Graph-Based Representations in Pattern Recognition

13th IAPR-TC-15 International Workshop, GbRPR 2023, Vietri sul Mare, Italy, September 6–8, 2023,...

Mario Vento, Pasquale Foggia, Donatello Conte , Vincenzo Carletti

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English

Springer International Publishing AG
24 August 2023

Discrete mathematics; Algorithms & data structures; Graphics programming; Maths for computer scientists; Pattern recognition

Series: Lecture Notes in Computer Science

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.

Edited by: Mario Vento, Pasquale Foggia, Donatello Conte, Vincenzo Carletti
Imprint: Springer International Publishing AG
Country of Publication: Switzerland
Edition: 1st ed. 2023
Volume: 14121
Dimensions: Height: 235mm, Width: 155mm,
Weight: 314g
ISBN: 9783031427947
ISBN 10: 3031427947
Series: Lecture Notes in Computer Science
Pages: 184
Publication Date: 24 August 2023
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

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.