APA (7th ed.) Citation

Kim, J., Glass, H. C., Amorim, E., Rao, V. R., Bernardo, D., Li, G., . . . Sanger, T. (2025). Comparison of Feature Engineering and End-to-End Machine Learning for Neonatal Preictal State Classification. Pediatric and Lifespan Data Science : First International Conference, IPLDSC 2024, Anaheim, CA, USA, May 23–24, 2024, Revised Selected Papers, 2386, 17-30. https://doi.org/10.1007/978-3-031-88346-0_2

Chicago Style (17th ed.) Citation

Kim, Jonathan, et al. "Comparison of Feature Engineering and End-to-End Machine Learning for Neonatal Preictal State Classification." Pediatric and Lifespan Data Science : First International Conference, IPLDSC 2024, Anaheim, CA, USA, May 23–24, 2024, Revised Selected Papers 2386 (2025): 17-30. https://doi.org/10.1007/978-3-031-88346-0_2.

MLA (9th ed.) Citation

Kim, Jonathan, et al. "Comparison of Feature Engineering and End-to-End Machine Learning for Neonatal Preictal State Classification." Pediatric and Lifespan Data Science : First International Conference, IPLDSC 2024, Anaheim, CA, USA, May 23–24, 2024, Revised Selected Papers, vol. 2386, 2025, pp. 17-30, https://doi.org/10.1007/978-3-031-88346-0_2.