Name
Abstract | ||
|---|---|---|
•A novel Minimum Error Minimax Probability Machine (MEMPM) method is presented.•The Cobb-Douglas production function is extended to machine learning.•The proposal is a robust formulation for linear and kernel-based classification.•The method is solved via a self-developed two-step alternating algorithm.•We prove that the optimization scheme converges to the optimal solution of the problem.•Best performance is achieved in experiments carried out on 17 benchmark datasets. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.patcog.2022.108701 | Pattern Recognition |
Keywords | DocType | Volume |
Cobb-Douglas,Minimax Probability Machine,Minimum Error Minimax Probability Machine,Second-order Cone Programming,Support Vector Machines | Journal | 128 |
ISSN | Citations | PageRank |
0031-3203 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sebastián Maldonado | 1 | 508 | 32.45 |
| Julio López | 2 | 124 | 13.49 |
| Miguel Carrasco | 3 | 21 | 4.35 |