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Can I read Dataset Shift in Machine Learning (Neural Information Processing series) on EtoBox?
Dataset Shift in Machine Learning (Neural Information Processing series) by Joaquin Quiñonero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D. Lawrence, eds. is a computer science book available to read on EtoBox.
What is Dataset Shift in Machine Learning (Neural Information Processing series) about?
<b>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.</b><p>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 prac
Who reads Dataset Shift in Machine Learning (Neural Information Processing series)?
It is typically read by working professionals who need an authoritative practice reference.
Common subject areas: medicine, law, business, engineering.
- Author
- Joaquin Quiñonero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D. Lawrence, eds.
- Publisher
- The MIT Press
- Published
- 2008
- Language
- EN
- ISBN
- 9780262255103
- Category
- computer science
- Subjects
- Science, Computer Science, Artificial Intelligence (Ai)
- Rating
- 4 / 5 (1 ratings)
- Updated
- 2026-03-25
Other editions & translations
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