References

Bishop, Christopher M. 2006. Pattern Recognition and Machine Learning. Vol. 2. Information Science and Statistics. Springer, New York.

Di Donna, Mariano Catello, Giuseppe Cucinella, Giulia Zaccaria, Giuseppe Lo Re, Agata Crapanzano, Sergio Salerno, Vincenzo Giallombardo, et al. 2023. “Concordance of Radiological, Laparoscopic and Laparotomic Scoring to Predict Complete Cytoreduction in Women with Advanced Ovarian Cancer.” Cancers 15 (2): 500. https://doi.org/10.3390/cancers15020500.

Hastie, T., R. Tibshirani, and J. H. Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics. Springer.

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2021. An Introduction to Statistical Learning: With Applications in R. 2nd ed. New York: Springer. https://doi.org/10.1007/978-1-0716-1418-1.

Malavi, Derick, Amin Nikkhah, Katleen Raes, and Sam Van Haute. 2023. “Hyperspectral Imaging and Chemometrics for Authentication of Extra Virgin Olive Oil: A Comparative Approach with FTIR, UV-VIS, Raman, and GC-MS.” Foods 12 (3): 429. https://doi.org/10.3390/foods12030429.

Street, W. Nick, W. H. Wolberg, and O. L. Mangasarian. 1993. “Nuclear Feature Extraction for Breast Tumor Diagnosis.” In Biomedical Image Processing and Biomedical Visualization, 1905:861–70. SPIE. https://doi.org/10.1117/12.148698.

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