 | 2011 |
| 13 |  | Shohei Hido,
Yuta Tsuboi,
Hisashi Kashima,
Masashi Sugiyama,
Takafumi Kanamori:
Statistical outlier detection using direct density ratio estimation.
Knowl. Inf. Syst. 26(2): 309-336 (2011) |
| 2009 |
| 12 |  | Shohei Hido,
Hisashi Kashima:
A Linear-Time Graph Kernel.
ICDM 2009: 179-188 |
| 11 |  | Shohei Hido,
Hirofumi Matsuzawa,
Fumihiko Kitayama,
Masayuki Numao:
Trace Mining from Distributed Assembly Databases for Causal Analysis.
PAKDD 2009: 731-740 |
| 10 |  | Yuta Tsuboi,
Hisashi Kashima,
Shohei Hido,
Steffen Bickel,
Masashi Sugiyama:
Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation.
JIP 17: 138-155 (2009) |
| 9 |  | Takafumi Kanamori,
Shohei Hido,
Masashi Sugiyama:
A Least-squares Approach to Direct Importance Estimation.
Journal of Machine Learning Research 10: 1391-1445 (2009) |
| 8 |  | Shohei Hido,
Hisashi Kashima,
Yutaka Takahashi:
Roughly balanced bagging for imbalanced data.
Statistical Analysis and Data Mining 2(5-6): 412-426 (2009) |
| 2008 |
| 7 |  | Shohei Hido,
Yuta Tsuboi,
Hisashi Kashima,
Masashi Sugiyama,
Takafumi Kanamori:
Inlier-Based Outlier Detection via Direct Density Ratio Estimation.
ICDM 2008: 223-232 |
| 6 |  | Takafumi Kanamori,
Shohei Hido,
Masashi Sugiyama:
Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection.
NIPS 2008: 809-816 |
| 5 |  | Shohei Hido,
Tsuyoshi Idé,
Hisashi Kashima,
Harunobu Kubo,
Hirofumi Matsuzawa:
Unsupervised Change Analysis Using Supervised Learning.
PAKDD 2008: 148-159 |
| 4 |  | Shohei Hido,
Hisashi Kashima:
Roughly Balanced Bagging for Imbalanced Data.
SDM 2008: 143-152 |
| 3 |  | Yuta Tsuboi,
Hisashi Kashima,
Shohei Hido,
Steffen Bickel,
Masashi Sugiyama:
Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation.
SDM 2008: 443-454 |
| 2007 |
| 2 |  | Shohei Hido,
Hiroyuki Kawano:
A fast algorithm for mining frequent ordered subtrees.
Systems and Computers in Japan 38(7): 34-43 (2007) |
| 2005 |
| 1 |  | Shohei Hido,
Hiroyuki Kawano:
AMIOT: Induced Ordered Tree Mining in Tree-Structured Databases.
ICDM 2005: 170-177 |