LSTM classifiers require larger noise statistic separations than model-based EM classifiers to achieve reliable binary time series classification and saturate below the optimal Kalman filter reference when models differ only in measurement noise.
Mod el-based deep learning for maneuvering target tracking,
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An Objective Performance Evaluation of the LSTM Networks in Time Series Classification
LSTM classifiers require larger noise statistic separations than model-based EM classifiers to achieve reliable binary time series classification and saturate below the optimal Kalman filter reference when models differ only in measurement noise.