  We propose a supervised learning algorithm for machine learning applications.
Contrary to the model developing in the classical methods, which treat
training, validation, and test as separate steps, in the presented approach,
there is a unified training and evaluating procedure based on an iterative band
filtering by the use of a fast Fourier transform. The presented approach does
not apply the method of least squares, thus, basically typical ill-conditioned
matrices do not occur at all. The optimal model results from the convergence of
the performance metric, which automatically prevents the usual underfitting and
overfitting problems. The algorithm capability is investigated for noisy data,
and the obtained result demonstrates a reliable and powerful machine learning
approach beyond the typical limits of the classical methods.
