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Biomathematics. Biometry. Biostatistics --- Molecular biology --- Programming --- Computer architecture. Operating systems --- Information systems --- Computer. Automation --- IR (information retrieval) --- biomathematica --- data mining --- biostatistiek --- biometrie --- database management --- computernetwerken --- moleculaire biologie

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Mathematical statistics --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- patroonherkenning --- ICT (informatie- en communicatietechnieken) --- IR (information retrieval) --- factoranalyse --- data mining --- informatiesystemen --- database management --- KI (kunstmatige intelligentie) --- robots

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The two-volume set LNAI 7818 + LNAI 7819 constitutes the refereed proceedings of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013, held in Gold Coast, Australia, in April 2013. The total of 98 papers presented in these proceedings was carefully reviewed and selected from 363 submissions. They cover the general fields of data mining and KDD extensively, including pattern mining, classification, graph mining, applications, machine learning, feature selection and dimensionality reduction, multiple information sources mining, social networks, clustering, text mining, text classification, imbalanced data, privacy-preserving data mining, recommendation, multimedia data mining, stream data mining, data preprocessing and representation.

Information retrieval --- Computer science --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- IR (information retrieval) --- machine learning --- data mining --- computers --- multimedia --- informatiesystemen --- database management --- KI (kunstmatige intelligentie) --- computerkunde --- robots --- data acquisition

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Deep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. The new neural network architectures on graph-structured data (graph neural networks, GNNs in short) have performed remarkably on these tasks, demonstrated by applications in social networks, bioinformatics, and medical informatics. Despite these successes, GNNs still face many challenges ranging from the foundational methodologies to the theoretical understandings of the power of the graph representation learning. This book provides a comprehensive introduction of GNNs. It first discusses the goals of graph representation learning and then reviews the history, current developments, and future directions of GNNs. The second part presents and reviews fundamental methods and theories concerning GNNs while the third part describes various frontiers that are built on the GNNs. The book concludes with an overview of recent developments in a number of applications using GNNs. This book is suitable for a wide audience including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications.

Mathematical statistics --- Computer science --- Programming --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- patroonherkenning --- factoranalyse --- datamining --- computers --- programmeren (informatica) --- KI (kunstmatige intelligentie) --- computerkunde --- data acquisition

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Mathematical statistics --- Computer architecture. Operating systems --- Information systems --- Computer. Automation --- patroonherkenning --- ICT (informatie- en communicatietechnieken) --- IR (information retrieval) --- factoranalyse --- informatiesystemen --- database management --- computernetwerken