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Survival neural networks for time-to-event prediction in longitudinal study.

, , , , , , and . Knowl. Inf. Syst., 62 (9): 3727-3751 (2020)

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A Variable-Order Regime Switching Model to Identify Significant Patterns in Financial Markets., , , and . ICDM, page 887-892. IEEE Computer Society, (2018)Survival neural networks for time-to-event prediction in longitudinal study., , , , , , and . Knowl. Inf. Syst., 62 (9): 3727-3751 (2020)Unsupervised Learning via Graph Convolutional Network for Stock Trend Prediction., , , and . AINA (1), volume 661 of Lecture Notes in Networks and Systems, page 358-369. Springer, (2023)A Hierarchical Clustering for Categorical Data Based on Holo-Entropy., , , and . IEEE WISA, page 269-274. IEEE Computer Society, (2015)Time-Dependent Survival Neural Network for Remaining Useful Life Prediction., , , , , and . PAKDD (1), volume 11439 of Lecture Notes in Computer Science, page 441-452. Springer, (2019)Dynamic Cross-sectional Regime Identification for Financial Market Prediction., , , and . COMPSAC, page 295-300. IEEE, (2022)Holo-Entropy Based Categorical Data Hierarchical Clustering., , , and . Informatica, 28 (2): 303-328 (2017)Clustering-Based Cross-Sectional Regime Identification for Financial Market Forecasting., , , , and . DEXA (2), volume 13427 of Lecture Notes in Computer Science, page 3-16. Springer, (2022)Dynamic order Markov model for categorical sequence clustering., , , , and . J. Big Data, 8 (1): 154 (2021)