March 29, 2016 — November 2, 2023

Figure 1

Quantisation of system state-space, considered in the abstract. Implicit in coding theory, compression, and mixture models.

Connection to classification.

A placeholder.

1 References

Gerber, Pospisil, Navandar, et al. 2020. Low-Cost Scalable Discretization, Prediction, and Feature Selection for Complex Systems.” Science Advances.
Horenko. 2020. On a Scalable Entropic Breaching of the Overfitting Barrier in Machine Learning.”
Horenko, Vecchi, Kardoš, et al. 2023. On Cheap Entropy-Sparsified Regression Learning.” Proceedings of the National Academy of Sciences.
Peluffo-Ordónez, Lee, and Verleysen. 2014. Short Review of Dimensionality Reduction Methods Based on Stochastic Neighbour Embedding.” In Advances in Self-Organizing Maps and Learning Vector Quantization.
Smola, Williamson, Mika, et al. 1999. Regularized Principal Manifolds.” In Computational Learning Theory. Lecture Notes in Computer Science 1572.
Vecchi, Pospíšil, Albrecht, et al. 2022. eSPA+: Scalable Entropy-Optimal Machine Learning Classification for Small Data Problems.” Neural Computation.