| Budget Amount *help |
¥17,030,000 (Direct Cost: ¥13,100,000、Indirect Cost: ¥3,930,000)
Fiscal Year 2019: ¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2018: ¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2017: ¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2016: ¥5,330,000 (Direct Cost: ¥4,100,000、Indirect Cost: ¥1,230,000)
|
|---|
| Outline of Final Research Achievements |
We have developed a method to suppress the appearance of hubs by converting the distance between data so that the data density is uniform. In particular, as an important graph construction method in graph-based semi-supervised learning, we proposed a method that does not have hubs and does not require an excessive reduction in the number of edges. Furthermore, when we investigated whether hubness occurred in bio-sequence data i.e., whether a specific sequence was similar to many other sequences, we confirmed that hubness occurred.
|
