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Dataset shift in machine learning /
This work is an overview of recent efforts in the machine learning community to deal with dataset and covariate shift which occurs when test and training inputs and outputs have different distributions.
| Clasificación: | Libro Electrónico |
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| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cambridge, Mass. :
MIT Press,
©2009.
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| Colección: | Neural information processing series.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- I. Introduction to dataset shift
- 1. When training and test sets are different: characterizing learning transfer / Amos Storkey
- 2. Projection and projectability / David Corfield
- II. Theoretical views on dataset and covariate shift
- 3. Binary classification under sample selection bias / Matthias Hein
- 4. On Bayesian transduction: implications for the covariate shift problem / Lars Kai Hansen
- 5. On the training/test distributions gap: a data representation learning framework / Shai Ben-David
- III. Algorithms for covariate shift
- 6. Geometry of covariate shift with applications to active learning / Takafumi Kanamori and Hidetoshi Shimodaira
- 7. A conditional expectation approach to model selection and active learning under covariate shift / Masashi Sugiyama, Neil Rubens and Klaus-Robert Muller
- 8. Covariate shift by kernel mean matching / Arthur Grellon, Alex Smola, Jiayuan Huang, Marcel Schmittfull, Karsten Borgwardt and Bernhard Scholkopf
- 9. Discriminative learning under covariate shift with a single optimization problem / Steffen Bickel, Michael Bruckner and Tobias Scheffer
- 10. An adversarial view of covariate shift and a minimax approach / Amir Globerson, Choon Hui Teo, Alex Smola and Sam Roweis
- IV. Discussion
- 11. Author comments / Hidetoshi Shimodaira, Masashi Sugiyama, Amos Storkey, Arthur Gretton and Shai-Ben David.