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- Takafumi Kanamori, Shohei Hido, Masashi Sugiyama
- Journal of Machine Learning Research
- 2009

We address the problem of estimating the ratio of two probability density functions, which is often referred to as the importance. The importance values can be used for various succeeding tasks such as covariate shift adaptation or outlier detection. In this paper, we propose a new importance estimation method that has a closed-form solution; the… (More)

- Takafumi Kanamori, Shohei Hido, Masashi Sugiyama
- NIPS
- 2008

We address the problem of estimating the ratio of two probability density functions (a.k.a. the importance). The importance values can be used for various succeeding tasks such as non-stationarity adaptation or outlier detection. In this paper, we propose a new importance estimation method that has a closed-form solution; the leave-one-out cross-validation… (More)

- Noboru Murata, Takashi Takenouchi, Takafumi Kanamori, Shinto Eguchi
- Neural Computation
- 2004

We aim at an extension of AdaBoost to U-Boost, in the paradigm to build a stronger classification machine from a set of weak learning machines. A geometric understanding of the Bregman divergence defined by a generic convex function U leads to the U-Boost method in the framework of information geometry extended to the space of the finite measures over a… (More)

- Masashi Sugiyama, Ichiro Takeuchi, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Daisuke Okanohara
- IEICE Transactions
- 2010

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,… (More)

Divergence estimators based on direct approximation of density ratios without going through separate approximation of numerator and denominator densities have been successfully applied to machine learning tasks that involve distribution comparison such as outlier detection, transfer learning, and two-sample homogeneity test. However, since density-ratio… (More)

- Masashi Sugiyama, Takafumi Kanamori, +4 authors Liwei Wang
- IPSJ Trans. Computer Vision and Applications
- 2009

In statistical pattern recognition, it is important to avoid density estimation since density estimation is often more difficult than pattern recognition itself. Following this idea—known as Vapnik's principle, a statistical data processing framework that employs the ratio of two probability density functions has been developed recently and is gathering a… (More)

- Taiji Suzuki, Masashi Sugiyama, Takafumi Kanamori, Jun Sese
- BMC Bioinformatics
- 2009

Although microarray gene expression analysis has become popular, it remains difficult to interpret the biological changes caused by stimuli or variation of conditions. Clustering of genes and associating each group with biological functions are often used methods. However, such methods only detect partial changes within cell processes. Herein, we propose a… (More)

- Shohei Hido, Yuta Tsuboi, Hisashi Kashima, Masashi Sugiyama, Takafumi Kanamori
- Knowledge and Information Systems
- 2010

We propose a new statistical approach to the problem of inlier-based outlier detection, i.e., finding outliers in the test set based on the training set consisting only of inliers. Our key idea is to use the ratio of training and test data densities as an outlier score. This approach is expected to have better performance even in high-dimensional problems… (More)

- Taiji Suzuki, Masashi Sugiyama, Jun Sese, Takafumi Kanamori
- FSDM
- 2008

Mutual information is useful in various data processing tasks such as feature selection or independent component analysis. In this paper, we propose a new method of approximating mutual information based on maximum likelihood estimation of a density ratio function. Our method, called Maximum Likelihood Mutual Information (MLMI), has several attractive… (More)

- Shohei Hido, Yuta Tsuboi, Hisashi Kashima, Masashi Sugiyama, Takafumi Kanamori
- 2008 Eighth IEEE International Conference on Data…
- 2008

We propose a new statistical approach to the problem of inlier-based outlier detection, i.e.,finding outliers in the test set based on the training set consisting only of inliers. Our key idea is to use the ratio of training and test data densities as an outlier score; we estimate the ratio directly in a semi-parametric fashion without going through density… (More)