Name
Abstract | ||
|---|---|---|
This paper presents a new algorithm based on the theory of mutual information and information geometry. This algorithm places emphasis on adaptive mutual information estimation and maximum likelihood estimation. With the theory of information geometry, we adjust the mutual information along the geodesic line. Finally, we evaluate our proposal using empirical datasets that are dedicated for classification and regression. The results show that our algorithm contributes to a significant improvement over existing methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/a12050103 | ALGORITHMS |
Keywords | Field | DocType |
neural networks,information geometry,geodesic line | Data mining,Information geometry,Regression,Maximum likelihood,Artificial intelligence,Mutual information,Artificial neural network,Mathematics,Machine learning,Geodesic | Journal |
Volume | Issue | ISSN |
12 | 5 | 1999-4893 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meng Wang | 1 | 1 | 0.69 |
| Chuangbai Xiao | 2 | 40 | 16.05 |
| Zhen-Hu Ning | 3 | 7 | 5.51 |
| Jing Yu | 4 | 123 | 20.30 |
| Ya-Hao Zhang | 5 | 0 | 0.34 |
| Jin Pang | 6 | 0 | 0.34 |