| Project/Area Number |
22700191
|
|---|
| Research Category |
Grant-in-Aid for Young Scientists (B)
|
| Allocation Type | Single-year Grants |
|---|
| Research Field |
Perception information processing/Intelligent robotics
|
|---|
| Research Institution | National Institute of Advanced Industrial Science and Technology |
|---|
Principal Investigator |
WATANABE Kenji 独立行政法人産業技術総合研究所, フェロー, 産総研特別研究員 (50571064)
|
|---|
| Research Collaborator |
OTSU Nobuyuki 独立行政法人産業技術総合研究所, フェロー
KOBAYASHI Takumi 独立行政法人産業技術総合研究所, 情報技術研究部門, 研究員 (30443188)
|
|---|
| Project Period (FY) |
2010 – 2011
|
| Project Status |
Completed (Fiscal Year 2011)
|
| Budget Amount *help |
¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
Fiscal Year 2011: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2010: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
|
|---|
| Keywords | ロジスティック回帰 / 数量化IV類 / 半教師あり機械学習手法 / 最適化 / 統計的パターン認識 / 機械学習手法 / 多変量解析 / 付与ラベルの確信度推定手法 |
|---|
| Research Abstract |
In the analysis of real data such as the biological signals, the given labels are often unreliable. Because, objects to be measured inherently contain some physical and biological uncertainty, and some labels might be incorrectly assigned by human intuition. Whereas, reliable labels would be available for a small portion of the samples. In such case, a semi-supervised learning method is effectively applied to analyze the data, estimating the label values of samples. In addition, it is favorable that the estimated label values provide us the degree of confidence of each sample. In this research, we proposed a novel method of semi-supervised learning, incorporating logistic functions into label propagation in order to accurately estimate the label values as the posterior probabilities. We call this method logistic label propagation(LLP). In addition, we proposed a novel optimization method for LR by directly using the non-linear conjugate gradient method in order to apply to LLP and to reduce the computational cost. Our proposed methods achieve the better estimation of degree of confidence and the faster computation times compared with the ordinary methods.
|
