Construction of optimal (stable and of highest possible accuracy) regression models comprising of linear combination of independent variables and their non-linear functions is considered. It is shown that estimates of the experimental error, which are most often available for engineers and experimental scientists, are useful for identifying the set of variables to be included in an optimal regr...

The analysis of time complexity of evolutionary algorithms has always focused on some artificial binary problems. This paper considers the average time complexity of an evolutionary algorithm for maximum cardinality matching in a graph. It is shown that the evolutionary algorithm can produce matchings with nearly maximum cardinality in average polynomial time.

We investigate a series of learning kernel problems with polynomial combinations of base kernels, which will help us solve regression and classification problems. We also perform some numerical experiments of polynomial kernels with regression and classification tasks on different datasets.