Broad Learning Through Fusions
Springer International Publishing (Verlag)
978-3-030-12527-1 (ISBN)
Jiawei Zhang is Assistant Professor in the Department of Computer Science at Florida State University. In 2017 he founded IFM Lab, a research oriented academic laboratory, providing the latest information on fusion learning and data mining research works and application tools to both academia and industry. Philip S. Yu is Professor in the Department of Computer Science at the University of Illinois at Chicago and also holds the Wexler Chair in Information and Technology. He was manager of the Software Tools and Techniques group at the IBM Thomas J. Watson Research Center. Dr. Yu has published more than 500 papers in refereed journals and conferences. He holds or has applied for more than 300 US patents.
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.
Erscheinungsdatum | 30.06.2019 |
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Zusatzinfo | XV, 419 p. 104 illus., 81 illus. in color. |
Verlagsort | Cham |
Sprache | englisch |
Maße | 178 x 254 mm |
Gewicht | 1004 g |
Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
Schlagworte | Data Mining • data structures • fusion learning • machine learning • network alignment • Social Networks |
ISBN-10 | 3-030-12527-0 / 3030125270 |
ISBN-13 | 978-3-030-12527-1 / 9783030125271 |
Zustand | Neuware |
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