Bayesian geometric theory of learning algorithms

The problem of objective evaluation of learning algorithms is analyzed under the principles of coherence and covariance. The theory of Bayesian information geometry satisfies these principles and encompasses most of the commonly used learning criteria. Implications to learning theory are discussed.