  A general information transmission model, under independent and identically
distributed Gaussian codebook and nearest neighbor decoding rule with processed
channel output, is investigated using the performance metric of generalized
mutual information. When the encoder and the decoder know the statistical
channel model, it is found that the optimal channel output processing function
is the conditional expectation operator, thus hinting a potential role of
regression, a classical topic in machine learning, for this model. Without
utilizing the statistical channel model, a problem formulation inspired by
machine learning principles is established, with suitable performance metrics
introduced. A data-driven inference algorithm is proposed to solve the problem,
and the effectiveness of the algorithm is validated via numerical experiments.
Extensions to more general information transmission models are also discussed.
