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cs.LG2025
Learning the Optimal Stopping for Early Classification within Finite Horizons via Sequential Probability Ratio Test
Akinori F. Ebihara, Taiki Miyagawa, Kazuyuki Sakurai +1
Time-sensitive machine learning benefits from Sequential Probability Ratio Test (SPRT), which provides an optimal stopping time for early classification of time series. However, in…
cs.LG2020
Sequential Density Ratio Estimation for Simultaneous Optimization of Speed and Accuracy
Akinori F. Ebihara, Taiki Miyagawa, Kazuyuki Sakurai +1
Classifying sequential data as early and as accurately as possible is a challenging yet critical problem, especially when a sampling cost is high. One algorithm that achieves this…