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Destinazione, tempi e costiDa: Lucky's Textbooks, Dallas, TX, U.S.A.
Condizione: New. Codice articolo ABLING22Oct2817100110483
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Da: PBShop.store US, Wood Dale, IL, U.S.A.
HRD. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Codice articolo L1-9781522590965
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Da: PBShop.store UK, Fairford, GLOS, Regno Unito
HRD. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Codice articolo L1-9781522590965
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Da: Ria Christie Collections, Uxbridge, Regno Unito
Condizione: New. In. Codice articolo ria9781522590965_new
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Da: moluna, Greven, Germania
Condizione: New. KlappentextrnrnLink prediction is required to understand the evolutionary theory of computing for different social networks. However, the stochastic growth of the social network leads to various challenges in identifying hidden links, such as re. Codice articolo 448011631
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Da: AHA-BUCH GmbH, Einbeck, Germania
Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Link prediction is required to understand the evolutionary theory of computing for different social networks. However, the stochastic growth of the social network leads to various challenges in identifying hidden links, such as representation of graph, distinction between spurious and missing links, selection of link prediction techniques comprised of network features, and identification of network types. Hidden Link Prediction in Stochastic Social Networks concentrates on the foremost techniques of hidden link predictions in stochastic social networks including methods and approaches that involve similarity index techniques, matrix factorization, reinforcement, models, and graph representations and community detections. The book also includes miscellaneous methods of different modalities in deep learning, agent-driven AI techniques, and automata-driven systems and will improve the understanding and development of automated machine learning systems for supervised, unsupervised, and recommendation-driven learning systems. It is intended for use by data scientists, technology developers, professionals, students, and researchers. Codice articolo 9781522590965
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Da: Revaluation Books, Exeter, Regno Unito
Hardcover. Condizione: Brand New. 281 pages. 10.00x7.00x0.83 inches. In Stock. Codice articolo x-152259096X
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