Abstract
Transfer learning is an important branch of machine learning. They have very tight connections. Therefore, we should first start familiarizing with the basics of machine learning. With these bases, we can then deeply understand their problems with more insights. We have briefly introduced the background and concepts of transfer learning in the last chapter. We will dive into this area starting from this chapter. We introduce the definition of machine learning and probability distribution. Then, we present the definition of transfer learning, along with its fundamental problems and negative transfer case. Finally, we present a complete transfer learning process.
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Notes
- 1.
The definition in this book is different from the definitions in Pan and Yang (2010) and Yang et al. (2020): they defined a domain as \(\mathcal {D} = (\mathcal {X}, P(\boldsymbol {x}))\) and further introduced a new concept called task: \(\mathcal {T} = (\mathcal {Y}, f)\). Since this book focuses on the algorithms in domain adaptation, our definition of a domain stems from the very natural data generating process ((x, y) ∼ P(x, y)), which also includes joint probability distributions. Interested readers can find that our definitions and theirs are only different in forms, but the same in spirit.
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Wang, J., Chen, Y. (2023). From Machine Learning to Transfer Learning. In: Introduction to Transfer Learning. Machine Learning: Foundations, Methodologies, and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-19-7584-4_2
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DOI: https://doi.org/10.1007/978-981-19-7584-4_2
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