| 2008 |
|---|
| 12 | EE | Masanori Kawakita,
Shinto Eguchi:
Boosting Method for Local Learning in Statistical Pattern Recognition.
Neural Computation 20(11): 2792-2838 (2008) |
| 11 | EE | Takashi Takenouchi,
Shinto Eguchi,
Noboru Murata,
Takafumi Kanamori:
Robust Boosting Algorithm Against Mislabeling in Multiclass Problems.
Neural Computation 20(6): 1596-1630 (2008) |
| 2007 |
|---|
| 10 | EE | Takafumi Kanamori,
Takashi Takenouchi,
Shinto Eguchi,
Noboru Murata:
Robust Loss Functions for Boosting.
Neural Computation 19(8): 2183-2244 (2007) |
| 9 | EE | Md. Nurul Haque Mollah,
Shinto Eguchi,
Mihoko Minami:
Robust Prewhitening for ICA by Minimizing beta-Divergence and Its Application to FastICA.
Neural Processing Letters 25(2): 91-110 (2007) |
| 2006 |
|---|
| 8 | EE | Md. Nurul Haque Mollah,
Mihoko Minami,
Shinto Eguchi:
Exploring Latent Structure of Mixture ICA Models by the Minimum ß-Divergence Method.
Neural Computation 18(1): 166-190 (2006) |
| 2005 |
|---|
| 7 | EE | Takashi Takenouchi,
Masaru Ushijima,
Shinto Eguchi:
GroupAdaBoost for Selecting Important Genes.
BIBE 2005: 218-221 |
| 2004 |
|---|
| 6 | EE | Takafumi Kanamori,
Takashi Takenouchi,
Shinto Eguchi,
Noboru Murata:
The Most Robust Loss Function for Boosting.
ICONIP 2004: 496-501 |
| 5 | EE | Isao Higuchi,
Shinto Eguchi:
Robust Principal Component Analysis with Adaptive Selection for Tuning Parameters.
Journal of Machine Learning Research 5: 453-471 (2004) |
| 4 | EE | Takashi Takenouchi,
Shinto Eguchi:
Robustifying AdaBoost by Adding the Naive Error Rate.
Neural Computation 16(4): 767-787 (2004) |
| 3 | EE | Noboru Murata,
Takashi Takenouchi,
Takafumi Kanamori,
Shinto Eguchi:
Information Geometry of U-Boost and Bregman Divergence.
Neural Computation 16(7): 1437-1481 (2004) |
| 2002 |
|---|
| 2 | EE | Mihoko Minami,
Shinto Eguchi:
Robust Blind Source Separation by Beta Divergence.
Neural Computation 14(8): 1859-1886 (2002) |
| 1998 |
|---|
| 1 | | Isao Higuchi,
Shinto Eguchi:
The Influence Function of Principal Component Analysis by Self-Organizing Rule.
Neural Computation 10(6): 1435-1444 (1998) |
