[PDF.51ab] Dataset Shift in Machine Learning (Neural Information Processing series)

Dataset Shift in Machine Learning (Neural Information Processing series)

[PDF.zg42] Dataset Shift in Machine Learning (Neural Information Processing series)

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 | #1908797 in Books |  2008-12-12 | Original language:English | PDF # 1 |  10.00 x.44 x8.00l,1.52 | File type: PDF | 248 pages

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||About the Author||Joaquin Quiñonero-Candela is a Researcher in the Online Services and Advertising Group at Microsoft Research Cambridge, U.K. Masashi Sugiyama is Associate Professor in the Department of Computer Science at the Tokyo Institute of Technolo

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 practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. (An example is ...

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