Download e-book for iPad: Dataset Shift in Machine Learning by Joaquin Quiñonero-Candela, Masashi Sugiyama, Anton

By Joaquin Quiñonero-Candela, Masashi Sugiyama, Anton Schwaighofer, Neil D. Lawrence

Dataset shift is a standard challenge in predictive modeling that happens while the joint distribution of inputs and outputs differs among education and attempt phases. Covariate shift, a specific case of dataset shift, happens while in basic terms the enter distribution adjustments. Dataset shift is found in such a lot useful functions, for purposes starting from the unfairness brought by means of experimental layout to the irreproducibility of the checking out stipulations at education time. (An instance is -email junk mail filtering, which could fail to acknowledge unsolicited mail that differs in shape from the unsolicited mail the automated clear out has been outfitted on.) regardless of this, and regardless of the eye given to the it seems that related difficulties of semi-supervised studying and lively studying, dataset shift has acquired particularly little awareness within the laptop studying group until eventually lately. This quantity bargains an outline of present efforts to house dataset and covariate shift. The chapters provide a mathematical and philosophical advent to the matter, position dataset shift in dating to move studying, transduction, neighborhood studying, lively studying, and semi-supervised studying, offer theoretical perspectives of dataset and covariate shift (including choice theoretic and Bayesian perspectives), and current algorithms for covariate shift. Contributors : Shai Ben-David, Steffen Bickel, Karsten Borgwardt, Michael Brückner, David Corfield, Amir Globerson, Arthur Gretton, Lars Kai Hansen, Matthias Hein, Jiayuan Huang, Takafumi Kanamori, Klaus-Robert Müller, Sam Roweis, Neil Rubens, Tobias Scheffer, Marcel Schmittfull, Bernhard Schölkopf, Hidetoshi Shimodaira, Alex Smola, Amos Storkey, Masashi Sugiyama, Choon Hui Teo Neural details Processing sequence

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This issue is discussed more generally in the next section. 11 Shift or No Shift? 27 Shift or No Shift? One big issue in all types of dataset shift is determining whether there is, in fact, any type of shift at all. It is possible that using a modeling method which can account for covariate shift may produce worse results than a standard model on data for which no shift occurs. This is first because introducing the possibility of shift allows for a large scope of possible representations that waters down the more concrete (but rigid) assumptions that presuming no shift makes.

For Gaussian processes, this computation is harder than in most parametric models as we cannot expect to be able to do standard expectation maximization. Expectation maximization algorithms involve iterative computation of responsibilities P (sμ ) for each data point μ and then a maximum-likelihood parameter estimation for the parameters given the responsibilities. However as Gaussian processes are nonparametric, the distribution is not independent of the allocation. Hence whether one point is allocated to one mixture or not will immediately affect the distribution over all other mixtures.

As an example, a pollster predicting election results has no recourse to the voting patterns of the population as a whole until it is too late. 28 When Training and Test Sets Are Different: Characterizing Learning Transfer to aid prediction. Multitask learning can be thought of a special case of transfer learning where there is some commonality in training covariates between tasks, and where the covariates have the same meaning across scenarios (hence domain shift is precluded). , 2007]. Unlike the methods developed here, this approach relies on having target data for all scenarios to help in relating them.

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