Buch, Englisch, 255 Seiten, Format (B × H): 155 mm x 235 mm
Joint IAPR International Workshops, S+SSPR 2026, Bern, Switzerland, August 24–26, 2026, Revised Selected Papers
Buch, Englisch, 255 Seiten, Format (B × H): 155 mm x 235 mm
Reihe: Lecture Notes in Computer Science
ISBN: 978-3-032-40097-0
Verlag: Springer
This book constitutes the proceedings of the Joint IAPR International Workshops on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2026, which took place in Bern, Switzerland, during August 2026.
The 23 full papers included in this book were carefully reviewed and selected from 38 submissions.
They were organised in topical sections as follows: Spectral and Structural Graph Neural Networks; Shape, Geometry, and Topological Representation; Adaptive Augmentation and Open-World Perception; Distance-Based and Uncertainty-Aware Classification; Biomedical and Molecular Representation Learning; Understanding Graph Structure and Graph Similarity; Latent Spaces, Multi-View Retrieval, and Clustering; and Graph-Based Analysis of Spatial and Networked Systems.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Grafikprogrammierung
- Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
- Mathematik | Informatik Mathematik Mathematik Allgemein Diskrete Mathematik, Kombinatorik
Weitere Infos & Material
.- Spectral and Structural Graph Neural Networks .
.- Dirichlet Machines.
.- HybridEigenLoRA: Depth-Adaptive Spectral Correction for Deep GCNs.
.- RegFidelity: A measure of the Interpretation quality for GNNs applied to Graph Regression.
.- Shape, Geometry, and Topological Representation .
.- True egg-shapes.
.- Graph-Structural and Topological Features for 3D Mesh Saliency.
.- Generalized Conic Fitting for Interpretable Shape Representation with Applications to Biomedicine.
.- Adaptive Augmentation and Open-World Perception .
.- Generating Synthetic Data with Diffusion Models for Writer Identification.
.- MAPTTA: Metamer-based Attribute Patching for Test-Time Augmentation in GNNs.
.- Distance-Based and Uncertainty-Aware Classification .
.- Evaluating Random Forest measures for distance-based classification of time series.
.- Fighting Data Scarcity: Weighted Three Way Decision Cascade Classifiers.
.- Analysis of elastic distances for distance-based classification of volcano seismic events.
.- Biomedical and Molecular Representation Learning .
.- A Lightweight Hybrid CNN–Biomarker Fusion Framework for Explainable Glaucoma Detection.
.- Probing Subject-Level Attributes in Brain Age Prediction Models.
.- Database augmentation for SARS-CoV-2 Mpro Binding-affinity Prediction.
.- Understanding Graph Structure and Graph Similarity .
.- On the Predictability of Graph Structure from Node Features.
.- A Graph Transformer for Node Classification with Gated Structural Attention.
.- On the Complexity of Graph Edit Distance in Restricted Graph Classes.
.- Latent Spaces, Multi-View Retrieval, and Clustering .
.- Machine Learning Without the Pre-image Problem Thanks to Normalizing Flows.
.- Orthogonality and Dimensionality in Airline Cluster Analysis using PCA and Kernel PCA.
.- Beyond Single-Space Representation: Adaptive Multi-View Neighborhood Retrieval for Tabular Learning.
.- Graph-Based Analysis of Spatial and Networked Systems .
.- Urban Functional Partition Based on Quantum Walk and Graph Convolutional Network.
.- Graph Representation Matters: Structure Analysis in Swiss River Networks.
.- Enhancing Long-Tail Event Detection in Autonomous Driving Using XAI-Guided Graph Clustering.
