Da: Better World Books, Mishawaka, IN, U.S.A.
Prima edizione
Condizione: Very Good. 1st Edition. Former library book; may include library markings. Used book that is in excellent condition. May show signs of wear or have minor defects.
Da: GreatBookPrices, Columbia, MD, U.S.A.
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Lingua: Inglese
Editore: Springer International Publishing AG, Cham, 2016
ISBN 10: 3319289217 ISBN 13: 9783319289212
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
Prima edizione
Paperback. Condizione: new. Paperback. Thiswork presents link prediction similarity measures for social networks that exploitthe degree distribution of the networks. In the context of link prediction indense networks, the text proposes similarity measures based on Markov inequalitydegree thresholding (MIDTs), which only consider nodes whose degree is above a thresholdfor a possible link. Also presented are similarity measures based on cliques(CNC, AAC, RAC), which assign extra weight between nodes sharing a greater numberof cliques. Additionally, a locally adaptive (LA) similarity measure isproposed that assigns different weights to common nodes based on the degreedistribution of the local neighborhood and the degree distribution of thenetwork. In the context of link prediction in dense networks, the textintroduces a novel two-phase framework that adds edges to the sparse graph toforma boost graph. In the context of link prediction indense networks, the text proposes similarity measures based on Markov inequalitydegree thresholding (MIDTs), which only consider nodes whose degree is above a thresholdfor a possible link. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Da: Ria Christie Collections, Uxbridge, Regno Unito
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Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 75,85
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Lingua: Inglese
Editore: Springer-Verlag New York Inc, 2016
ISBN 10: 3319289217 ISBN 13: 9783319289212
Da: Revaluation Books, Exeter, Regno Unito
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Aggiungi al carrelloPaperback. Condizione: Brand New. 80 pages. 9.00x6.00x0.25 inches. In Stock.
Da: Majestic Books, Hounslow, Regno Unito
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2016
ISBN 10: 365995652X ISBN 13: 9783659956522
Da: Books Puddle, New York, NY, U.S.A.
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Da: Ria Christie Collections, Uxbridge, Regno Unito
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Da: Biblios, Frankfurt am main, HESSE, Germania
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Editore: LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659860387 ISBN 13: 9783659860386
Da: moluna, Greven, Germania
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ISBN 10: 3319289217 ISBN 13: 9783319289212
Da: AHA-BUCH GmbH, Einbeck, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Thiswork presents link prediction similarity measures for social networks that exploitthe degree distribution of the networks. In the context of link prediction indense networks, the text proposes similarity measures based on Markov inequalitydegree thresholding (MIDTs), which only consider nodes whose degree is above a thresholdfor a possible link. Also presented are similarity measures based on cliques(CNC, AAC, RAC), which assign extra weight between nodes sharing a greater numberof cliques. Additionally, a locally adaptive (LA) similarity measure isproposed that assigns different weights to common nodes based on the degreedistribution of the local neighborhood and the degree distribution of thenetwork. In the context of link prediction in dense networks, the textintroduces a novel two-phase framework that adds edges to the sparse graph toforma boost graph.
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ISBN 10: 3659860387 ISBN 13: 9783659860386
Da: Revaluation Books, Exeter, Regno Unito
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Aggiungi al carrelloPaperback. Condizione: Brand New. 172 pages. 8.66x5.91x0.39 inches. In Stock.
Da: Mispah books, Redhill, SURRE, Regno Unito
EUR 98,34
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Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2016
ISBN 10: 365995652X ISBN 13: 9783659956522
Da: Revaluation Books, Exeter, Regno Unito
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Aggiungi al carrelloPaperback. Condizione: Brand New. 216 pages. 8.66x5.91x0.49 inches. In Stock.
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Da: Revaluation Books, Exeter, Regno Unito
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Aggiungi al carrelloPaperback. Condizione: Brand New. 280 pages. 9.21x6.14x0.60 inches. In Stock.
Da: Buchpark, Trebbin, Germania
EUR 45,46
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Aggiungi al carrelloCondizione: Sehr gut. Zustand: Sehr gut | Seiten: 280 | Sprache: Englisch | Produktart: Bücher | This valuable text addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. Organized into eight chapters, the book begins by introducing PR, data mining, and knowledge discovery concepts. The authors proceed to analyze the tasks of multi-scale data condensation and dimensionality reduction. Then they explore the problem of learning with support vector machine (SVM), and conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm.
Lingua: Inglese
Editore: Springer International Publishing AG, Cham, 2016
ISBN 10: 3319289217 ISBN 13: 9783319289212
Da: AussieBookSeller, Truganina, VIC, Australia
Prima edizione
EUR 133,56
Quantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: new. Paperback. Thiswork presents link prediction similarity measures for social networks that exploitthe degree distribution of the networks. In the context of link prediction indense networks, the text proposes similarity measures based on Markov inequalitydegree thresholding (MIDTs), which only consider nodes whose degree is above a thresholdfor a possible link. Also presented are similarity measures based on cliques(CNC, AAC, RAC), which assign extra weight between nodes sharing a greater numberof cliques. Additionally, a locally adaptive (LA) similarity measure isproposed that assigns different weights to common nodes based on the degreedistribution of the local neighborhood and the degree distribution of thenetwork. In the context of link prediction in dense networks, the textintroduces a novel two-phase framework that adds edges to the sparse graph toforma boost graph. In the context of link prediction indense networks, the text proposes similarity measures based on Markov inequalitydegree thresholding (MIDTs), which only consider nodes whose degree is above a thresholdfor a possible link. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 161,64
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Da: GreatBookPrices, Columbia, MD, U.S.A.
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