Name
Abstract | ||
|---|---|---|
A new concept of machine learning based on the computer simulation of entropy-optimal randomized models is proposed. The procedures of randomized machine learning (RML) with “hard” and “soft” randomization are considered; the former imply the exact reproduction of empirical balances while the latter their rough reproduction with an accepted approximation criterion. RML algorithms are formulated as functional entropy-linear programming problems. Applications of RML procedures to text classification and the randomized forecasting of migratory interaction of regional systems are presented. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1134/S0005117919090078 | Automation and Remote Control |
Keywords | Field | DocType |
randomization, hard and soft randomization procedures, uncertainty, entropy, matrix norms, empirical balances, text classification, dynamic regression | Matrix norm,Artificial intelligence,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
80 | 9 | 0005-1179 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu. S. Popkov | 1 | 2 | 2.46 |