Lite-RVFL assigns exponentially increasing weights to recent samples, yielding a closed-form incremental update that adapts to concept drift on a single real-world dataset without drift detection.
Real-time safety assessment of dynamic systems in non-stationary environments: A review of methods and techniques,
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Lite-RVFL: A Lightweight Random Vector Functional-Link Neural Network for Learning Under Concept Drift
Lite-RVFL assigns exponentially increasing weights to recent samples, yielding a closed-form incremental update that adapts to concept drift on a single real-world dataset without drift detection.