Kolmogorov Complexity Bounds Neural Network Generalization via Algorithmic Information Theory — E8 Intelligence Research

FINDING: Kolmogorov complexity provides a lower bound on generalization in neural networks via algorithmic information theory. | MATH: \( K(x) = \min_{p} \{ |p| : U(p) = x \} \), where \( U \) is a universal Turing machine. Generalization error bound: \( \epsilon \leq \sqrt{\frac{K(\text{model}) + \log(1/\delta)}{n}} \) (PAC-Bayes style). | CONNECTION: No direct geometric ratios or symmetries; the core link is to algorithmic randomness and compression, not harmonic or crystallographic patterns. | DEPTH: 7 — profound for linking computation, randomness, and learning, but lacks explicit geometric harmony. Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com