UID:
almahu_9949385878602882
Umfang:
1 online resource (xv, 97 pages) :
,
illustrations
ISBN:
9781000025408
,
1000025403
,
9781000025361
,
1000025365
,
9780429270352
,
0429270356
Serie:
Data-enabled engineering
Inhalt:
In recent years social media has gained significant popularity and has become an essential medium of communication. Such user-generated content provides an excellent scenario for applying the metaphor of mining any information. Transfer learning is a research problem in machine learning that focuses on leveraging the knowledge gained while solving one problem and applying it to a different, but related problem. Features: Offers novel frameworks to study user behavior and for addressing and explaining task heterogeneity Presents a detailed study of existing research Provides convergence and complexity analysis of the frameworks Includes algorithms to implement the proposed research work Covers extensive empirical analysis Social Media Analytics for User Behavior Modeling: A Task Heterogeneity Perspective is a guide to user behavior modeling in heterogeneous settings and is of great use to the machine learning community.
Anmerkung:
Introduction -- Literature survey -- Social media for diabetes managment -- Learning from task heterogencity -- Explainable transfer learning -- Conclusion.
Weitere Ausg.:
Print version: ISBN 9781000025408
Weitere Ausg.:
Print version: ISBN 0367211580
Weitere Ausg.:
ISBN 9780367211585
Sprache:
Englisch
Schlagwort(e):
Electronic books.
;
Electronic books.
;
Electronic books.
DOI:
10.1201/9780429270352
URL:
https://www.taylorfrancis.com/books/9780429270352
