تقرير
Copula-based conformal prediction for Multi-Target Regression
| العنوان: | Copula-based conformal prediction for Multi-Target Regression |
|---|---|
| المؤلفون: | Messoudi, Soundouss, Destercke, Sébastien, Rousseau, Sylvain |
| سنة النشر: | 2021 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning, 68T07 |
| الوصف: | There are relatively few works dealing with conformal prediction for multi-task learning issues, and this is particularly true for multi-target regression. This paper focuses on the problem of providing valid (i.e., frequency calibrated) multi-variate predictions. To do so, we propose to use copula functions applied to deep neural networks for inductive conformal prediction. We show that the proposed method ensures efficiency and validity for multi-target regression problems on various data sets. Comment: 17 pages, 8 figures, under review |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2101.12002 |
| رقم الأكسشن: | edsarx.2101.12002 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |