Classification Using Support Vector Machines with Uncertainty Quantification
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Binary classification using machine learning is needed to address engineering problems such as identifying passing/failing parts based on measured features from aging hardware. In these classifications, providing the uncertainty of each prediction is essential to support engineering decision making. One popular classifier is the support vector machine (SVM). There are many variations, with the simplest being a linear division between two classes with a hyperplane. Kernel methods can be implement
- Research Organization:
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- Other (NNSA)
- DOE Contract Number:
- NA0003525
- OSTI ID:
- 2584949
- Report Number(s):
- SAND2024-06425C; 1734860
- Country of Publication:
- United States
- Language:
- English
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