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  • Broad Learning Through Fusions: An Application on Social Networks

    Broad Learning Through Fusions by Zhang, Jiawei; Yu, Philip S.;

    An Application on Social Networks

      • GET 20% OFF

      • The discount is only available for 'Alert of Favourite Topics' newsletter recipients.
      • Publisher's listprice EUR 53.49
      • The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.

        22 184 Ft (21 128 Ft + 5% VAT)
      • Discount 20% (cc. 4 437 Ft off)
      • Discounted price 17 748 Ft (16 902 Ft + 5% VAT)

    22 184 Ft

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    Availability

    printed on demand

    Why don't you give exact delivery time?

    Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.

    Long description:

    This book offers a clear and comprehensive introduction to broad learning, one of the novel learning problems studied in data mining and machine learning. Broad learning aims at fusing multiple large-scale information sources of diverse varieties together, and carrying out synergistic data mining tasks across these fused sources in one unified analytic. This book takes online social networks as an application example to introduce the latest alignment and knowledge discovery algorithms. Besides the overview of broad learning, machine learning and social network basics, specific topics covered in this book include network alignment, link prediction, community detection, information diffusion, viral marketing, and network embedding.

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    Table of Contents:

    1 Broad Learning Introduction.- 2 Machine Learning Overview.- 3 Social Network Overview.- 4 Supervised Network Alignment.- 5 Unsupervised Network Alignment.- 6 Semi-supervised Network Alignment.- 7 Link Prediction.- 8 Community Detection.- 9 Information Diffusion.- 10 Viral Marketing.- 11 Network Embedding.- 12 Frontier and Future Directions.- References.


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