#### Filter Results:

- Full text PDF available (240)

#### Publication Year

1991

2018

- This year (40)
- Last 5 years (138)
- Last 10 years (290)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Brain Region

#### Data Set Used

#### Method

Learn More

- Masashi Sugiyama
- Journal of Machine Learning Research
- 2007

Reducing the dimensionality of data without losing intrinsic information is an important preprocessing step in high-dimensional data analysis. Fisher discriminant analysis (FDA) is a traditionalâ€¦ (More)

A situation where training and test samples follow different input distributions is calledcovariate shift . Under covariate shift, standard learning methods such as maximum likelihood estimation areâ€¦ (More)

- Masashi Sugiyama
- ICML
- 2006

Dimensionality reduction is one of the important preprocessing steps in high-dimensional data analysis. In this paper, we consider the supervised dimensionality reduction problem where samples areâ€¦ (More)

- Masashi Sugiyama, Tsuyoshi IdÃ©, Shinichi Nakajima, Jun Sese
- Machine Learning
- 2008

When only a small number of labeled samples are available, supervised dimensionality reduction methods tend to perform poorly because of overfitting. In such cases, unlabeled samples could be usefulâ€¦ (More)

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

We address the problem of estimating the ratio of two probabi lity density functions, which is often referred to as theimportance. The importance values can be used for various succeeding ta sks suchâ€¦ (More)

- Masashi Sugiyama
- 2008

A situation where training and test samples follow different input distributions is called covariate shift. Under covariate shift, standard learning methods such as maximum likelihood estimation areâ€¦ (More)

- Song Liu, Makoto Yamada, Nigel Collier, Masashi Sugiyama
- SSPR/SPR
- 2012

The objective of change-point detection is to discover abrupt property changes lying behind time-series data. In this paper, we present a novel statistical change-point detection algorithm based onâ€¦ (More)

- Masashi Sugiyama, Matthias Krauledat, Klaus-Robert MÃ¼ller
- Journal of Machine Learning Research
- 2007

A common assumption in supervised learning is that the input points in the training set follow the same probability distribution as the input points that will be given in the future test phase.â€¦ (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 toâ€¦ (More)

- Yoshinobu Kawahara, Masashi Sugiyama
- SDM
- 2009

Change-point detection is the problem of discovering time points at which properties of time-series data change. This covers a broad range of real-world problems and has been actively discussed inâ€¦ (More)