| Autor: |
Akusok, Anton, Miche, Yoan, Björk, Kaj-Mikael, Lendasse, Amaury |
| Rok vydání: |
2019 |
| Předmět: |
|
| Zdroj: |
Int. J. Mach. Learn. & Cyber. (2019) 10: 991 |
| Druh dokumentu: |
Working Paper |
| Popis: |
Prediction intervals in supervised Machine Learning bound the region where the true outputs of new samples may fall. They are necessary in the task of separating reliable predictions of a trained model from near random guesses, minimizing the rate of False Positives, and other problem-specific tasks in applied Machine Learning. Many real problems have heteroscedastic stochastic outputs, which explains the need of input-dependent prediction intervals. This paper proposes to estimate the input-dependent prediction intervals by a separate Extreme Learning Machine model, using variance of its predictions as a correction term accounting for the model uncertainty. The variance is estimated from the model's linear output layer with a weighted Jackknife method. The methodology is very fast, robust to heteroscedastic outputs, and handles both extremely large datasets and insufficient amount of training data. |
| Databáze: |
arXiv |
| Externí odkaz: |
|
