training sample

The paper considers methods for optimizing training samples for deep learning algorithms through the use of random point processes, such as Matern of the first and second types, Gibbs, Gaussian, and Poisson processes. An approach to reducing training data without sacrificing informativeness is proposed, enabling a decrease in computational costs and mitigating the risk of overfitting.

The problem of convergence of the procedure for synthesizing classifier schemes in the methods of logical and algorithmic classification trees is considered. An upper estimate of the complexity of the algorithm tree scheme is proposed in the problem of approximating an array of real data with a set of generalized features with a fixed criterion for stopping the branching procedure at the stage of constructing a classification tree.