Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
The Journal of Machine Learning Research
Content-based image retrieval for semiconductor process characterization
EURASIP Journal on Applied Signal Processing
Covariate Shift Adaptation by Importance Weighted Cross Validation
The Journal of Machine Learning Research
Market intelligence and price adaptation
Proceedings of the 14th Annual International Conference on Electronic Commerce
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The general setting of regression analysis is to identify a relationship between a response variable Y and one or several explanatory variables X by using a learning sample. In a prediction framework, the main assumption for predicting Y on a new sample of observations is that the regression model Y=f(X)+@e is still valid. Unfortunately, this assumption is not always true in practice and the model could have changed. We therefore propose to adapt the original regression model to the new sample by estimating a transformation between the original regression function f(X) and the new one f^*(X). The main interest of the proposed adaptive models is to allow the build of a regression model for the new population with only a small number of observations using the knowledge on the reference population. The efficiency of this strategy is illustrated by applications on artificial and real datasets, including the modeling of the housing market in different U.S. cities. A package for the R software dedicated to the adaptive linear models is available on the author's web page.