Computationally lightweight classifiers with frequentist bounds on predictions
While both classical and neural network classifiers can achieve high accuracy, they fall short on offering uncertainty bounds on their predictions, making them unfit for safety-critical applications. Existing kernel-based classifiers that provide such bounds scale with $\mathcal O (n^{\sim3})$ in time, making them computationally intractable for large datasets. To address this, we propose a novel, computationally efficient classification algorithm based on the Nadaraya-Watson estimator, for whose estimates we derive frequentist uncertainty intervals. We evaluate our classifier on synthetically generated data and on electrocardiographic heartbeat signals from the MIT-BIH Arrhythmia databas...
| Authors | Shreeram Murali, Cristian R. Rojas, Dominik Baumann |
|---|---|
| Primary category | cs.LG |
| Published | 23 March 2026 |
| Last updated at source | 9 April 2026 |
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