Estimation of Kolmogorov-Chaitin Complexity on Continuous Biomedical Data for Machine Learning Purposes

Author:

Iapascurta VictorORCID

Publisher

Springer Nature Switzerland

Reference8 articles.

1. Zenil, H.: A review of methods for estimating algorithmic complexity: options, challenges, and new directions. Entropy 22(6), 612 (2020)

2. Kolmogorov, A.N.: Three approaches to the quantitative definition of information. Probl. Inf. Transm. 1, 1–7 (1965)

3. Zenil, H., Soler-Toscano, F., Gauvrit, N., et al.: The Online Algorithmic Complexity Calculator (OACC) v3.0. www.algorithmicdynamics.net/software.html. Accessed 27 Feb 2024

4. Reyna, M., et al.: Early Prediction of Sepsis from Clinical Data: the PhysioNet Computing in Cardiology Challenge 2019. https://doi.org/10.13026/v64v-d857. Accessed 20 Jan 2024

5. American Epilepsy Society Seizure Prediction Challenge (2014). https://www.kaggle.com/c/seizure-prediction. Accessed 20 Jan 2024

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