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Dataset Shift in Machine Learning

Dataset Shift in Machine Learning - Paperback

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Availability:In StockContributor:Joaquin Quinonero-Candela (Editor), Masashi Sugiyama (Editor), Anton Schwaighofer (Editor)Series:Neural Information ProcessingAudience:Young AdultPublish date:2022-06-07Pages:248
Language:EnglishPublisher:MIT PressISBN-13:9780262545877ISBN-10:026254587XUPC:9780262545877Book Category:ComputersBook Subcategory:Machine Theory, Data Science, Artificial IntelligenceSize:10.00 x 8.00 x 0.52 inchesWeight:1.0913Product ID:SC1Y8EQDRG

Dataset Shift in Machine Learning

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...
Series: Neural Information Processing
Audience: Young Adult
Language:EnglishPublisher:MIT PressISBN-13:9780262545877ISBN-10:026254587XUPC:9780262545877Book Category:ComputersBook Subcategory:Machine Theory, Data Science, Artificial IntelligenceSize:10.00 x 8.00 x 0.52 inchesWeight:1.0913Product ID:SC1Y8EQDRG
Joaquin Quiñonero-Candela
Joaquin Quiñonero-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...


Publisher: MIT Press

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