Cantitate/Preț
Produs
Total produse
Vezi coșul
Machine Learning Techniques for Online Social Networks de Tansel Özyer

Machine Learning Techniques for Online Social Networks: Lecture Notes in Social Networks

Editat de Tansel Özyer, Reda Alhajj
en Limba Engleză Hardback – 31 mai 2018
The book covers tools in the study of online social networks such as machine learning techniques, clustering, and deep learning. A variety of theoretical aspects, application domains, and case studies for analyzing social network data are covered. The aim is to provide new perspectives on utilizing machine learning and related scientific methods and techniques for social network analysis. Machine Learning Techniques for Online Social Networks will appeal to researchers and students in these fields. 

Citește tot Restrânge

Toate formatele și edițiile

Toate formatele și edițiile Preț Express
Paperback (1) 50277 lei  38-44 zile
  Springer International Publishing – 14 dec 2018 50277 lei  38-44 zile
Hardback (1) 58772 lei  6-8 săpt.
  Springer International Publishing – 31 mai 2018 58772 lei  6-8 săpt.

Din seria Lecture Notes in Social Networks

Preț: 58772 lei

Preț vechi: 69143 lei
-15% Nou

Puncte Express: 882
Preț estimativ în valută:
11248€ • 11604$ • 9519£

Carte tipărită la comandă

Livrare economică 04-18 martie

 Adaugă în coș

Wish list
Gift list
Am citit!
Adaugă în listă
Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9783319899312
ISBN-10: 3319899317
Pagini: 253
Ilustrații: VIII, 236 p. 102 illus., 85 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.52 kg
Ediția:1st ed. 2018
Editura: Springer International Publishing
Colecția Springer
Seria Lecture Notes in Social Networks

Locul publicării:Cham, Switzerland

Cuprins

Chapter1. Acceleration of Functional Cluster Extraction and Analysis of Cluster Affinity.- Chapter2. Delta-Hyperbolicity and the Core-Periphery Structure in Graphs.- Chapter3. A Framework for OSN Performance Evaluation Studies.- Chapter4. On The Problem of Multi-Staged Impression Allocation in Online Social Networks.- Chapter5. Order-of-Magnitude Popularity Estimation of Pirated Content.- Chapter6. Learning What to Share in Online Social Networks using Deep Reinforcement Learning.- Chapter7. Centrality and Community Scoring Functions in Incomplete Networks: Their Sensitivity, Robustness and Reliability.- Chapter8. Ameliorating Search Results Recommendation System based on K-means Clustering Algorithm and Distance Measurements.- Chapter9. Dynamics of large scale networks following a merger.- Chapter10. Cloud Assisted Personal Online Social Network.- Chapter11. Text-Based Analysis of Emotion by Considering Tweets.

Notă biografică

Tansel Özyer is an associate professor of Computer Engineering at TOBB University of Economics and Technology, Turkey. He completed his PhD in Computer Science, University of Calgary. He received his MSc and BSc from Computer Engineering departments of METU and Bilkent University. Research interests are data mining, social network analysis, machine learning, bioinformatics, XML, mobile databases, and computer vision.

Reda Alhajj is a professor in the Department of Computer Science at the University of Calgary. He published over 500 papers in refereed international journals and conferences. He is founding editor in chief of the Springer premier journal “Social Networks Analysis and Mining”, founding editor-in-chief of Springer Series “Lecture Notes on Social Networks”, founding editor-in-chief of Springer journal “Network Modeling Analysis in Health Informatics and Bioinformatics”, founding co-editor-in-chief of Springer “Encyclopedia on Social NetworksAnalysis and Mining”, founding steering chair of IEEE/ACM ASONAM, and three accompanying symposiums FAB, FOSINT-SI and HI-BI-BI. Dr. Alhajj's research concentrates primarily on data science from management to integration and analysis.

Textul de pe ultima copertă

The book covers tools in the study of online social networks such as machine learning techniques, clustering, and deep learning. A variety of theoretical aspects, application domains, and case studies for analyzing social network data are covered. The aim is to provide new perspectives on utilizing machine learning and related scientific methods and techniques for social network analysis. Machine Learning Techniques for Online Social Networks will appeal to researchers and students in these fields. 

Caracteristici

Editors are widely known and well established scholars in social network analysis Covers the link between machine learning techniques and social networks Contains case studies describing how various domains may benefit from online social networks