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Introduction to Transfer Learning : Algorithms and Practice / by Jindong Wang, Yiqiang Chen
(Machine Learning: Foundations, Methodologies, and Applications. ISSN:27309916)

データ種別	電子ブック
版	1st ed. 2023.
出版者	(Singapore : Springer Nature Singapore : Imprint: Springer)
出版年	2023
大きさ	XXI, 329 p. 1 illus. in color : online resource
著者標目	*Wang, Jindong author Chen, Yiqiang author SpringerLink (Online service)

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URL	巻　次	配架場所	請求記号	登録番号	コメント	刷　年	状　態	利用注記	ISBN	予約	請求メモ
URL		射水-電子	007	EB0002609	Computer Scinece R0 2005-6,2022-3				9789811975844

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一般注記	Part 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 Transfer 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 HTTP:URL=https://doi.org/10.1007/978-981-19-7584-4
件　名	LCSH:Machine learning LCSH:Computer science LCSH:Computer vision LCSH:Natural language processing (Computer science) FREE:Machine Learning FREE:Theory and Algorithms for Application Domains FREE:Computer Vision FREE:Natural Language Processing (NLP)
分　類	LCC:Q325.5-.7 DC23:006.31
書誌ID	EB00001997
ISBN	9789811975844