Name
Abstract | ||
|---|---|---|
•Practical solution developed for quality control in a manufacturing process.•Analysis detects overfitting of the optimization objective.•Detailed investigation confirms optimization is beneficial despite overfitting. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.asoc.2017.05.027 | Applied Soft Computing |
Keywords | Field | DocType |
Quality control,Machine vision,Machine learning,Optimization,Overfitting | Early stopping,Errors-in-variables models,Mathematical optimization,Computer science,Supervised learning,Pruning (decision trees),Artificial intelligence,Structural risk minimization,Overfitting,Random forest,Cross-validation,Machine learning | Journal |
Volume | Issue | ISSN |
59 | C | 1568-4946 |
Citations | PageRank | References |
1 | 0.35 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tea Tusar | 1 | 181 | 19.91 |
| Klemen Gantar | 2 | 1 | 0.35 |
| Valentin Koblar | 3 | 1 | 0.35 |
| Bernard Zenko | 4 | 1 | 0.35 |
| Bogdan Filipic | 5 | 361 | 26.93 |