TY  - BOOK
AU  - Quiñonero-Candela,Joaquin
TI  - Dataset shift in machine learning
T2  - Neural information processing series
SN  - 9780262255103
AV  - Q325.5 .D37 2009eb
U1  - 006.3/1 22
PY  - 2009///
CY  - Cambridge, Mass.
PB  - MIT Press
KW  - Machine learning
KW  - Apprentissage automatique
KW  - COMPUTERS
KW  - Enterprise Applications
KW  - Business Intelligence Tools
KW  - bisacsh
KW  - Intelligence (AI) & Semantics
KW  - fast
KW  - COMPUTER SCIENCE/Machine Learning & Neural Networks
KW  - Electronic books
N1  - Includes bibliographical references (pages 207-218) and index; 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; Electronic reproduction; [Place of publication not identified]; HathiTrust Digital Library; 2010
N2  - 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
UR  - https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=259275
ER  -