@techreport{oai:ipsj.ixsq.nii.ac.jp:00066990,
 author = {Masashi, Sugiyama and Ichiro, Takeuchi and Taiji, Suzuki and Takafumi, Kanamori and Hirotaka, Hachiya and Daisuke, Okanohara and Masashi, Sugiyama and Ichiro, Takeuchi and Taiji, Suzuki and Takafumi, Kanamori and Hirotaka, Hachiya and Daisuke, Okanohara},
 issue = {4},
 month = {Dec},
 note = {Estimating the conditional mean of an input-output relation is the goal of regression. However, regression analysis is not sufficiently informative if the conditional distribution has multi-modality, is highly asymmetric, or contains heteroscedastic noise. In such scenarios, estimating the conditional distribution itself would be more useful. In this paper, we propose a novel method of conditional density estimation that is suitable for multi-dimensional continuous variables. The basic idea of the proposed method is to express the conditional density in terms of the density ratio and the ratio is directly estimated without going through density estimation., Estimating the conditional mean of an input-output relation is the goal of regression. However, regression analysis is not sufficiently informative if the conditional distribution has multi-modality, is highly asymmetric, or contains heteroscedastic noise. In such scenarios, estimating the conditional distribution itself would be more useful. In this paper, we propose a novel method of conditional density estimation that is suitable for multi-dimensional continuous variables. The basic idea of the proposed method is to express the conditional density in terms of the density ratio and the ratio is directly estimated without going through density estimation.},
 title = {Conditional Density Estimation Based on Density Ratio Estimation},
 year = {2009}
}