Name
Title | ||
|---|---|---|
Sublinear Computational Time Modeling by Momentum-Space Renormalization Group Theory in Statistical Machine Learning Procedures |
Abstract | ||
|---|---|---|
We review sublinear computational time modeling using momentum-space renormalization group approaches in the statistical machine learning algorithms. The modeling scheme has been proposed and the basic frameworks have been briefly explained in a short note (Tanaka et al. in J. Phys. Soc. Jpn, 87(8), Article ID: 085001, 2018). We present their detailed formulations and some numerical experimental results of sublinear computational time modeling based on the momentum-space renormalization scheme. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s12626-019-00053-1 | The Review of Socionetwork Strategies |
Keywords | Field | DocType |
Statistical machine learning, Bayesian modeling, Probabilistic graphical model, Renormalization group, Statistical mechanical informatics | Renormalization,Sublinear function,Position and momentum space,Economics,Bayesian inference,Artificial intelligence,Machine learning,Renormalization group | Journal |
Volume | Issue | ISSN |
13 | 2 | 2523-3173 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kazuyuki Tanaka | 1 | 0 | 0.68 |
| Masayuki Ohzeki | 2 | 8 | 3.50 |
| Muneki Yasuda | 3 | 9 | 7.79 |