Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation
نویسندگان
چکیده
منابع مشابه
Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation
Covariate shift is a situation in supervised learning where training and test inputs follow different distributions even though the functional relation remains unchanged. A common approach to compensating for the bias caused by covariate shift is to reweight the training samples according to importance, which is the ratio of test and training densities. We propose a novel method that allows us ...
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When training and test samples follow different input distributions (i.e., the situation called covariate shift), the maximum likelihood estimator is known to lose its consistency. For regaining consistency, the log-likelihood terms need to be weighted according to the importance (i.e., the ratio of test and training input densities). Thus, accurately estimating the importance is one of the key...
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ژورنال
عنوان ژورنال: Journal of Information Processing
سال: 2009
ISSN: 1882-6652
DOI: 10.2197/ipsjjip.17.138