Dataset Shift in Machine Learning
$70.86
- Paperback
248 pages
- Release Date
7 June 2022
Summary
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.
Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. Dataset shift is present in most pr…
Book Details
| ISBN-13: | 9780262545877 |
|---|---|
| ISBN-10: | 026254587X |
| Author: | Joaquin Quinonero-Candela, Masashi Sugiyama, Anton Schwaighofer, Neil D. Lawrence |
| Publisher: | MIT Press Ltd |
| Imprint: | MIT Press |
| Format: | Paperback |
| Number of Pages: | 248 |
| Release Date: | 7 June 2022 |
| Weight: | 369g |
| Dimensions: | 254mm x 203mm |
| Series: | Neural Information Processing series |
About The Author
Joaquin Quinonero-Candela
Joaquin Quinonero-Candela
Joaquin Quinonero-Candela is a Researcher in the Online Services and Advertising Group at Microsoft Research Cambridge, U.K.
Masashi Sugiyama
Masashi Sugiyama is Director of the RIKEN Center for Advanced Intelligence Project and Professor of Computer Science at the University of Tokyo.
Anton Schwaighofer
Anton Schwaighofer is an Applied Researcher in the Online Services and Advertising Group at Microsoft Research, Cambridge, U.K.
Neil D. Lawrence
Neil D. Lawrence is Senior Lecturer and Member of the Machine Learning and Optimisation Research Group in the School of Computer Science at the University of Manchester.
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