Intro; Preface; Organization; Contents; Experimental Evaluation of Subgraph Isomorphism Solvers; 1 Introduction; 2 Experimental Set-Up; 3 Does the Solving Time Depend on Graph Sizes?; 4 Where Are the Hard Instances?; 5 Experimental Comparison of the Solvers; 6 Combining Solvers to Take the Best of Them; 7 Conclusion; References; GEDLIB: A C++ Library for Graph Edit Distance Computation; 1 Introduction; 2 Overall Architecture; 3 User Interface; 4 Abstract Classes for Implementing GED Algorithms; 5 Abstract Class for Implementing Edit Costs; 6 Conclusions and Future Work; References
متن يادداشت
2.2 Mixed Integer Linear Program2.3 F3 Formulation; 3 VPLS Heuristic; 3.1 Main Features of VPLS; 3.2 VPLS for the GED Problem; 4 Computational Experiments; 5 Conclusion; References; A Database and Evaluation for Classification of RNA Molecules Using Graph Methods; 1 Introduction; 2 Related Work; 3 Database; 4 RNA Representation; 5 Classification Methods; 5.1 Sequence-Based Methods; 5.2 Weisfeiler-Lehman Optimal Assignment (WL-OA); 5.3 Shortest Path Embedding; 5.4 All Paths and Cycles Embedding(APC); 6 Results; 7 Conclusion; References
Graph Edge Entropy in Maxwell-Boltzmann Statistics for Alzheimer's Disease Analysis1 Introduction; 2 Graph Representation; 2.1 Preliminaries; 2.2 Von Neumann Edge Entropy; 3 Thermodynamic Statistics and Global Entropy Decomposition; 3.1 Thermodynamic Entropy; 3.2 Maxwell-Boltzmann Statistics; 3.3 Edge Entropy Decomposition; 4 Experiments and Evaluations; 4.1 Dataset; 4.2 Experimental Results; 5 Conclusion; References; Solving the Graph Edit Distance Problem with Variable Partitioning Local Search; 1 Introduction; 2 GED Definition and F3 Formulation; 2.1 GED Problem Definition
متن يادداشت
Learning the Graph Edit Costs: What Do We Want to Optimise?Abstract; 1 Introduction; 2 Attributed Graphs and Graph Edit Distance; 3 Learning Methods and Objective Functions; 4 Experimental Evaluation; 5 The Conclusions; Acknowledgments; References; Sub-optimal Graph Matching by Node-to-Node Assignment Classification; 1 Introduction; 2 Definitions; 2.1 Attributed Graphs and Graph Edit Distance; 2.2 Approximating the Graph Edit Distance; 3 Learning Graph Matching; 3.1 Learning the Edit Costs and Graph Embedding; 3.2 From Edit Costs Estimation to Node Assignment Classification
بدون عنوان
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بدون عنوان
8
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یادداشتهای مربوط به خلاصه یا چکیده
متن يادداشت
This book constitutes the refereed proceedings of the 12th IAPR-TC-15 International Workshop on Graph-Based Representation in Pattern Recognition, GbRPR 2019, held in Tours, France, in June 2019. The 22 full papers included in this volume together with an invited talk were carefully reviewed and selected from 28 submissions. The papers discuss research results and applications at the intersection of pattern recognition, image analysis, and graph theory. They cover topics such as graph edit distance, graph matching, machine learning for graph problems, network and graph embedding, spectral graph problems, and parallel algorithms for graph problems.
یادداشتهای مربوط به سفارشات
منبع سفارش / آدرس اشتراک
Springer Nature
شماره انبار
com.springer.onix.9783030200817
ویراست دیگر از اثر در قالب دیگر رسانه
شماره استاندارد بين المللي کتاب و موسيقي
9783030200800
شماره استاندارد بين المللي کتاب و موسيقي
9783030200824
عنوان اصلی به زبان دیگر
عنوان اصلي به زبان ديگر
GbRPR 2019
موضوع (اسم عام یاعبارت اسمی عام)
موضوع مستند نشده
Computer vision, Congresses.
موضوع مستند نشده
Graph theory, Congresses.
موضوع مستند نشده
Pattern recognition systems, Congresses.
موضوع مستند نشده
Computer vision.
موضوع مستند نشده
Graph theory.
موضوع مستند نشده
Pattern recognition systems.
مقوله موضوعی
موضوع مستند نشده
COM016000
موضوع مستند نشده
UYQP
موضوع مستند نشده
UYQP
رده بندی ديویی
شماره
006
.
3
ويراست
23
رده بندی کنگره
شماره رده
TK7882
.
P3
نام شخص - (مسئولیت معنوی برابر )
مستند نام اشخاص تاييد نشده
Conte, Donatello
مستند نام اشخاص تاييد نشده
Foggia, P., (Pasquale)
مستند نام اشخاص تاييد نشده
Ramel, Jean-Yves
نام تنالگان به منزله سر شناسه - (مسئولیت معنوی درجه اول )
مستند نام تنالگان تاييد نشده
IAPR-TC15 Workshop on Graph-Based Representations in Pattern Recognition(12th :2019 :, Tours, France)