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- W4223568283 abstract "Dynamic systems are highly complex and hard to deal with due to their subject- and time-varying nature. The fact that most of the real world systems/events are of dynamic character makes modeling and analysis of such systems inevitable and charmingly useful. One promising estimation method that is capable of unlearning past information to deal with non-stationarity is Stochastic Learning Weak Estimator (SLWE) by Oommen and Rueda (2006). However, due to using a constant learning rate, it faces a trade-off between plasticity and stability. In this paper, we model SLWE as a random walk and provide rigorous theoretical analysis of asymptotic behavior of estimates to obtain a statistical model. Utilizing this model, we detect changes in stationarity to switch between exploratory and exploitative learning modes. Experimental evaluations on both synthetic and real world data show that the proposed method outperforms related algorithms in different types of drifts." @default.
- W4223568283 created "2022-04-15" @default.
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- W4223568283 date "2022-09-01" @default.
- W4223568283 modified "2023-09-27" @default.
- W4223568283 title "An adaptive estimation method with exploration and exploitation modes for non-stationary environments" @default.
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- W4223568283 doi "https://doi.org/10.1016/j.patcog.2022.108702" @default.
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