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Structural, syntactic, and statistic...

International Workshop on Structural and Syntactic Pattern Recognition ((2024 :)

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Structural, syntactic, and statistical pattern recognition = Joint IAPR International Workshops, S+SSPR 2024, Venice, Italy, September 9-10, 2024 : revised selected papers /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Structural, syntactic, and statistical pattern recognition/ edited by Andrea Torsello ... [et al.].
其他題名:	Joint IAPR International Workshops, S+SSPR 2024, Venice, Italy, September 9-10, 2024 : revised selected papers /
其他題名:	S+SSPR 2024
其他作者:	Torsello, Andrea.
團體作者:	International Workshop on Structural and Syntactic Pattern Recognition
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	xii, 200 p. :ill. (chiefly color), digital ;24 cm.
內容註:	A Differentiable Approximation of the Graph Edit Distance. -- Learning Graph Similarity by Counting Holes in Simplicial Complexes. -- Community-Hop: Enhancing Node Classification through Community Preference. -- Spatio-Temporal Graph Neural Networks for Water Temperature Modeling. -- Enhancing IoT Network Security with Graph Neural Networks for Node Anomaly Detection. -- LSTM Networks and Graph Neural Networks for Predicting Events of Hypoglycemia. -- Evaluation metrics in Saliency Maps applied to Graph Regression. -- LESI-GNN: an Interpretable Graph Neural Network based on Local Structures Embedding. -- Mixture of Variational Graph Autoencoders. -- Multimodality Calibration in 3D Multi Input-Multi Output Network for Dementia Diagnosis with Incomplete Acquisitions. -- Multi-modal Medical Images Classification Using Meta-learning Algorithms. -- From semantic segmentation of natural images to medical image segmentation using ViT-based architectures. -- Chronic Wound Segmentation and Measurement Using Semi-Supervised Hierarchical Convolutional Neural Networks. -- ZIRACLE: Zero-shot composed Image Retrieval with Advanced Component-Level Emphasis. -- Improving Object Detector Performance on Low-Quality Images using Histogram Matching and Model Stacking. -- Comparing Learning Methods to Enhance Decision-Making in Simulated Curling. -- An empirical characterization of the stability of Isolation Forest results. -- Automated Classification of Android Games using Word Embeddings. -- An interesting property of Random Forest distances with respect to the curse of dimensionality.
Contained By:	Springer Nature eBook
標題:	Pattern recognition systems - Congresses. -
電子資源:	https://doi.org/10.1007/978-3-031-80507-3
ISBN:	9783031805073

Structural, syntactic, and statistical pattern recognition = Joint IAPR International Workshops, S+SSPR 2024, Venice, Italy, September 9-10, 2024 : revised selected papers /
Structural, syntactic, and statistical pattern recognitionJoint IAPR International Workshops, S+SSPR 2024, Venice, Italy, September 9-10, 2024 : revised selected papers /[electronic resource] :S+SSPR 2024edited by Andrea Torsello ... [et al.]. - Cham :Springer Nature Switzerland :2025. - xii, 200 p. :ill. (chiefly color), digital ;24 cm. - Lecture notes in computer science,154440302-9743 ;. - Lecture notes in computer science ;15444..

A Differentiable Approximation of the Graph Edit Distance. -- Learning Graph Similarity by Counting Holes in Simplicial Complexes. -- Community-Hop: Enhancing Node Classification through Community Preference. -- Spatio-Temporal Graph Neural Networks for Water Temperature Modeling. -- Enhancing IoT Network Security with Graph Neural Networks for Node Anomaly Detection. -- LSTM Networks and Graph Neural Networks for Predicting Events of Hypoglycemia. -- Evaluation metrics in Saliency Maps applied to Graph Regression. -- LESI-GNN: an Interpretable Graph Neural Network based on Local Structures Embedding. -- Mixture of Variational Graph Autoencoders. -- Multimodality Calibration in 3D Multi Input-Multi Output Network for Dementia Diagnosis with Incomplete Acquisitions. -- Multi-modal Medical Images Classification Using Meta-learning Algorithms. -- From semantic segmentation of natural images to medical image segmentation using ViT-based architectures. -- Chronic Wound Segmentation and Measurement Using Semi-Supervised Hierarchical Convolutional Neural Networks. -- ZIRACLE: Zero-shot composed Image Retrieval with Advanced Component-Level Emphasis. -- Improving Object Detector Performance on Low-Quality Images using Histogram Matching and Model Stacking. -- Comparing Learning Methods to Enhance Decision-Making in Simulated Curling. -- An empirical characterization of the stability of Isolation Forest results. -- Automated Classification of Android Games using Word Embeddings. -- An interesting property of Random Forest distances with respect to the curse of dimensionality.

This book constitutes the proceedings of the Joint IAPR International Workshops on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2024, which took place in Venice, Italy, during September 9-11, 2024. The 19 full papers presented in this volume were carefully reviewed and selected from 27 submissions. The proceedings focus on pattern recognition, including classification and clustering, deep learning, structural matching and graph-theoretic methods, and multimedia analysis and understanding.

ISBN: 9783031805073

Standard No.: 10.1007/978-3-031-80507-3doiSubjects--Topical Terms:

563039
Pattern recognition systems
--Congresses.

LC Class. No.: TK7882.P3

Dewey Class. No.: 006.4

Structural, syntactic, and statistical pattern recognition = Joint IAPR International Workshops, S+SSPR 2024, Venice, Italy, September 9-10, 2024 : revised selected papers /
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520 $a This book constitutes the proceedings of the Joint IAPR International Workshops on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2024, which took place in Venice, Italy, during September 9-11, 2024. The 19 full papers presented in this volume were carefully reviewed and selected from 27 submissions. The proceedings focus on pattern recognition, including classification and clustering, deep learning, structural matching and graph-theoretic methods, and multimedia analysis and understanding.
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