Synopsis
Many relationships among data in several areas (such as computer vision, molecular chemistry and pattern recognition) can be represented by graphs. In the machine learning setting, it is an important learning task to classify graph-structural data correctly. Typically, the established techniques for this setting proceed via graph kernels and neural-network classification. In this work, we explore end-to-end learning for graphs: the objective is to operate on the graph representations directly. The key idea of our approach is to use standard tools for graph canonization. We test the performance of this approach on several datasets arising from bioinformatics. In general, we find that the graph canonization, as such, does not improve the accuracy of the classification. A possible reason for this behavior is that the neural network ends up overfitting to the given adjacency matrix representation.
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End-to-end Graph Learning
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Many relationships among data in several areas (such as computer vision, molecular chemistry and pattern recognition) can be represented by graphs. In the machine learning setting, it is an important learning task to classify graph-structural data correctly. Typically, the established techniques for this setting proceed via graph kernels and neural-network classification. In this work, we explore end-to-end learning for graphs: the objective is to operate on the graph representations directly. The key idea of our approach is to use standard tools for graph canonization. We test the performance of this approach on several datasets arising from bioinformatics. In general, we find that the graph canonization, as such, does not improve the accuracy of the classification. A possible reason for this behavior is that the neural network ends up overfitting to the given adjacency matrix representation. 52 pp. Englisch. Seller Inventory # 9786202224178
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Yamen EmreEmre Yamen, studied Bachelor of Science Informatics at RWTH Aachen University. Master Student in Informatics and working on Machine Learning.Many relationships among data in several areas (such as computer vision, molec. Seller Inventory # 293729627
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End-to-end Graph Learning
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AV Akademikerverlag Mai 2019, 2019
ISBN 10: 6202224177
ISBN 13: 9786202224178
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Taschenbuch. Condition: Neu. Neuware -Many relationships among data in several areas (such as computer vision, molecular chemistry and pattern recognition) can be represented by graphs. In the machine learning setting, it is an important learning task to classify graph-structural data correctly. Typically, the established techniques for this setting proceed via graph kernels and neural-network classification. In this work, we explore end-to-end learning for graphs: the objective is to operate on the graph representations directly. The key idea of our approach is to use standard tools for graph canonization. We test the performance of this approach on several datasets arising from bioinformatics. In general, we find that the graph canonization, as such, does not improve the accuracy of the classification. A possible reason for this behavior is that the neural network ends up overfitting to the given adjacency matrix representation.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 52 pp. Englisch. Seller Inventory # 9786202224178
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End-to-end Graph Learning : Using Canonization
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Many relationships among data in several areas (such as computer vision, molecular chemistry and pattern recognition) can be represented by graphs. In the machine learning setting, it is an important learning task to classify graph-structural data correctly. Typically, the established techniques for this setting proceed via graph kernels and neural-network classification. In this work, we explore end-to-end learning for graphs: the objective is to operate on the graph representations directly. The key idea of our approach is to use standard tools for graph canonization. We test the performance of this approach on several datasets arising from bioinformatics. In general, we find that the graph canonization, as such, does not improve the accuracy of the classification. A possible reason for this behavior is that the neural network ends up overfitting to the given adjacency matrix representation. Seller Inventory # 9786202224178
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End-to-end Graph Learning | Using Canonization
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Taschenbuch. Condition: Neu. End-to-end Graph Learning | Using Canonization | Emre Yamen | Taschenbuch | 52 S. | Englisch | 2019 | AV Akademikerverlag | EAN 9786202224178 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Seller Inventory # 116800055