What is the connection between Naïve Bayes and entropy-regularized optimization?

Naïve Bayes can be interpreted as minimizing negative log-likelihood with implicit entropy regularization via smoothing.

Smoothing in NB introduces entropy into parameter estimation, preventing overly confident distributions. This can be viewed as optimizing a regularized objective combining likelihood maximization with entropy constraints. The effect is more stable probability estimates in sparse regimes.