Minimum Divergence Methods in Statistical Machine Learning
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Abstract
Individual chapters are listed in the "Chapters" tab
Suggested Citation
DOI: 10.1007/978-4-431-56922-0
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Book Chapters
The following chapters of this book are listed in IDEAS- Shinto Eguchi & Osamu Komori, 2022. "Information Geometry," Springer Books, in: Minimum Divergence Methods in Statistical Machine Learning, chapter 0, pages 3-17, Springer.
- Shinto Eguchi & Osamu Komori, 2022. "Information Divergence," Springer Books, in: Minimum Divergence Methods in Statistical Machine Learning, chapter 0, pages 19-69, Springer.
- Shinto Eguchi & Osamu Komori, 2022. "Maximum Entropy Model," Springer Books, in: Minimum Divergence Methods in Statistical Machine Learning, chapter 0, pages 71-95, Springer.
- Shinto Eguchi & Osamu Komori, 2022. "Minimum Divergence Method," Springer Books, in: Minimum Divergence Methods in Statistical Machine Learning, chapter 0, pages 97-122, Springer.
- Shinto Eguchi & Osamu Komori, 2022. "Unsupervised Learning Algorithms," Springer Books, in: Minimum Divergence Methods in Statistical Machine Learning, chapter 0, pages 125-152, Springer.
- Shinto Eguchi & Osamu Komori, 2022. "Regression Model," Springer Books, in: Minimum Divergence Methods in Statistical Machine Learning, chapter 0, pages 153-178, Springer.
- Shinto Eguchi & Osamu Komori, 2022. "Classification," Springer Books, in: Minimum Divergence Methods in Statistical Machine Learning, chapter 0, pages 179-195, Springer.
- Shinto Eguchi & Osamu Komori, 2022. "Outcome Weighted Learning in Dynamic Treatment Regimes," Springer Books, in: Minimum Divergence Methods in Statistical Machine Learning, chapter 0, pages 197-216, Springer.
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