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Graph-Based Representations in Pattern Recognition

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Cover of 'Graph-Based Representations in Pattern Recognition'

Table of Contents

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    Book Overview
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    Chapter 1 Quadratic Kernel Learning for Interpolation Kernel Machine Based Graph Classification
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    Chapter 2 Minimum Spanning Set Selection in Graph Kernels
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    Chapter 3 Graph-Based vs. Vector-Based Classification: A Fair Comparison
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    Chapter 4 A Practical Algorithm for Max-Norm Optimal Binary Labeling of Graphs
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    Chapter 5 An Efficient Entropy-Based Graph Kernel
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    Chapter 6 GNN-DES: A New End-to-End Dynamic Ensemble Selection Method Based on Multi-label Graph Neural Network
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    Chapter 7 C2N-ABDP: Cluster-to-Node Attention-Based Differentiable Pooling
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    Chapter 8 Splitting Structural and Semantic Knowledge in Graph Autoencoders for Graph Regression
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    Chapter 9 Graph Normalizing Flows to Pre-image Free Machine Learning for Regression
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    Chapter 10 Matching-Graphs for Building Classification Ensembles
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    Chapter 11 Maximal Independent Sets for Pooling in Graph Neural Networks
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    Chapter 12 Detecting Abnormal Communication Patterns in IoT Networks Using Graph Neural Networks
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    Chapter 13 Cell Segmentation of  in situ Transcriptomics Data Using Signed Graph Partitioning
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    Chapter 14 Graph-Based Representation for Multi-image Super-Resolution
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    Chapter 15 Reducing the Computational Complexity of the Eccentricity Transform of a Tree
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    Chapter 16 Graph-Based Deep Learning on the Swiss River Network
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Title
Graph-Based Representations in Pattern Recognition
Published by
Springer Nature Switzerland, October 2023
DOI 10.1007/978-3-031-42795-4
ISBNs
978-3-03-142794-7, 978-3-03-142795-4
Editors

Vento, Mario, Foggia, Pasquale, Conte, Donatello, Carletti, Vincenzo

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