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024 7 _a10.1007/978-981-19-7584-4
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082 0 4 _a006.31
_223
100 1 _aWang, Jindong.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aIntroduction to Transfer Learning
_h[electronic resource] :
_bAlgorithms and Practice /
_cby Jindong Wang, Yiqiang Chen.
250 _a1st ed. 2023.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2023.
300 _aXXI, 329 p. 1 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aMachine Learning: Foundations, Methodologies, and Applications,
_x2730-9916
505 0 _aPart I. Foundations of Transfer Learning -- Chapter 1. Introduction -- Chapter 2. From Machine Learning to Transfer Learning -- Chapter 3. Overview of Transfer Learning Algorithms -- Chapter 4. Instance Weighting Methods -- Chapter 5. Statistical Feature Transformation Methods -- Chapter 6. Geometrical Feature Transformation Methods -- Chapter 7. Theory, Evaluation, and Model Selection -- Part II. Modern Transfer Leaning -- Chapter 8. Pre-training and Fine-tuning -- Chapter 9. Deep Transfer Learning -- Chapter 10. Adversarial Transfer Learning -- Chapter 11. Generalization in Transfer Learning -- Chapter 12. Safe & Robust Transfer Learning -- Chapter 13. Transfer Learning in Complex Environments -- Chapter 14. Low-resource Learning -- Part III. Applications -- Chapter 15. Transfer Learning for Computer Vision -- Chapter 16. Transfer Learning for Natural language Processing -- Chapter 17. Transfer Learning for Speech Recognition -- Chapter 18. Transfer Learning for Activity Recognition -- Chapter 19. Federated Learning for Personalized Healthcare -- Chapter 20. Concluding Remarks.
520 _aTransfer learning is one of the most important technologies in the era of artificial intelligence and deep learning. It seeks to leverage existing knowledge by transferring it to another, new domain. Over the years, a number of relevant topics have attracted the interest of the research and application community: transfer learning, pre-training and fine-tuning, domain adaptation, domain generalization, and meta-learning. This book offers a comprehensive tutorial on an overview of transfer learning, introducing new researchers in this area to both classic and more recent algorithms. Most importantly, it takes a “student’s” perspective to introduce all the concepts, theories, algorithms, and applications, allowing readers to quickly and easily enter this area. Accompanying the book, detailed code implementations are provided to better illustrate the core ideas of several important algorithms, presenting good examples for practice.
650 0 _aMachine learning.
650 0 _aComputer science.
650 0 _aComputer vision.
650 0 _aNatural language processing (Computer science).
650 1 4 _aMachine Learning.
650 2 4 _aTheory and Algorithms for Application Domains.
650 2 4 _aComputer Vision.
650 2 4 _aNatural Language Processing (NLP).
700 1 _aChen, Yiqiang.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811975837
776 0 8 _iPrinted edition:
_z9789811975851
776 0 8 _iPrinted edition:
_z9789811975868
830 0 _aMachine Learning: Foundations, Methodologies, and Applications,
_x2730-9916
856 4 0 _uhttps://doi.org/10.1007/978-981-19-7584-4
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
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