Transfer Learning
- Authors:
- Qiang Yang, Hong Kong University of Science and Technology
- Yu Zhang, Hong Kong University of Science and Technology
- Wenyuan Dai, 4Paradigm Co., Ltd.
- Sinno Jialin Pan, Nanyang Technological University, Singapore
- Date Published: February 2020
- availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
- format: Adobe eBook Reader
- isbn: 9781108860086
Find out more about Cambridge eBooks
Adobe eBook Reader
Other available formats:
Hardback
Looking for an inspection copy?
Please email [email protected] to enquire about an inspection copy of this book
-
Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available. This makes such systems more reliable and robust, keeping the machine learning model faced with unforeseeable changes from deviating too much from expected performance. At an enterprise level, transfer learning allows knowledge to be reused so experience gained once can be repeatedly applied to the real world. For example, a pre-trained model that takes account of user privacy can be downloaded and adapted at the edge of a computer network. This self-contained, comprehensive reference text describes the standard algorithms and demonstrates how these are used in different transfer learning paradigms. It offers a solid grounding for newcomers as well as new insights for seasoned researchers and developers.
Read more- Distinguished authors who are pioneers of transfer learning research and practice.
- This is the first book on this important subfield of machine learning and artificial intelligence
- Featured applications include multimedia, Web search, text mining, sentiment analysis, cyber-physical systems, inference on social networks, and collaborative recommendation
Awards
- Choice Outstanding Academic Title 2020, Choice Reviews.
Reviews & endorsements
'Transfer learning is a critically important approach in settings where data is sparse or expensive. This comprehensive text focuses on when to transfer, what to transfer, and how to transfer previously learned knowledge into a novel current task. The authors cover historic methods as well as very recent methods, classifying them into a comprehensive ontology of transfer learning methods. Through its coverage of basic methods, advanced methods, and multiple application domains, the text will provide a useful guide to both novice and the experienced researchers and practitioners.' Matthew E. Taylor, Principal Researcher at Borealis AI, Edmonton
See more reviews'This book offers a comprehensive overview of the field, arguing the case for adaptation as key to mimicking human intelligence … The book includes a substantial bibliography documenting copious citations to the literature. There appear to be few other textbooks in this field apart from this unique work. As such, it will be welcomed by libraries supporting strong computer science programs that may have need for a core text in artificial intelligence.' D. Z. Spicer, Choice
Customer reviews
Not yet reviewed
Be the first to review
Review was not posted due to profanity
×Product details
- Date Published: February 2020
- format: Adobe eBook Reader
- isbn: 9781108860086
- contains: 143 b/w illus.
- availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
1. Introduction
2. Instance-based transfer learning
3. Feature-based transfer learning
4. Model-based transfer learning
5. Relation-based transfer learning
6. Heterogeneous transfer learning
7. Adversarial transfer learning
8. Transfer learning in reinforcement learning
9 Multi-task learning
10. Transfer learning theory
11. Transitive transfer learning
12. AutoTL: learning to transfer automatically
13. Few-shot learning
14. Lifelong machine learning
15. Privacy-preserving transfer learning
16. Transfer learning in computer vision
17. Transfer learning in natural language processing
18. Transfer learning in dialogue systems
19. Transfer learning in recommender systems
20. Transfer learning in bioinformatics
21. Transfer learning in activity recognition
22. Transfer learning in urban computing
23. Concluding remarks.
Sorry, this resource is locked
Please register or sign in to request access. If you are having problems accessing these resources please email [email protected]
Register Sign in» Proceed
You are now leaving the Cambridge University Press website. Your eBook purchase and download will be completed by our partner www.ebooks.com. Please see the permission section of the www.ebooks.com catalogue page for details of the print & copy limits on our eBooks.
Continue ×Are you sure you want to delete your account?
This cannot be undone.
Thank you for your feedback which will help us improve our service.
If you requested a response, we will make sure to get back to you shortly.
×