Ensemble learning method using structure information and its application
| Project/Area Number |
25730018
|
|---|
| Research Category |
Grant-in-Aid for Young Scientists (B)
|
| Allocation Type | Multi-year Fund |
|---|
| Research Field |
Statistical science
|
|---|
| Research Institution | Future University-Hakodate |
|---|
Principal Investigator |
Takenouchi Takashi 公立はこだて未来大学, システム情報科学部, 准教授 (50403340)
|
|---|
| Project Period (FY) |
2013-04-01 – 2017-03-31
|
| Project Status |
Completed (Fiscal Year 2016)
|
| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2015: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2014: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2013: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
|
|---|
| Keywords | アンサンブル学習 / 判別分析 / 情報幾何 / 板倉斎藤ダイバージェンス / 拡張擬似モデル / 板倉斉藤ダイバージェンス / 機械学習 |
|---|
| Outline of Final Research Achievements |
Multiclass classification problems sometimes require huge computational cost and then a major (and efficient) approach for the problem is to integrate multiple binary classifier. In this framework, we proposed a general framework of ensemble, which includes various kinds of conventional integration-based methods as special cases.
We proposed an ensemble-based method for the Multi-task problem. The proposed method is based on a combination of the Itakura-Saito distance and an extended model, rather than the conventional combination of the Kullback-Leibler divergence and statistical models. We revealed statistical properties of the proposed method and investigated validity of the proposed method.
|
Report
(5 results)
Research Products
(16 results)
