Multivariate data analysis based on the w(n)(k)-critoria and multilayer perceptron
A comparative study of multidimensional classifiers based on the goodness-of-fit criteria omega(n)(k) and multilayer perceptrons (MLP) has been carried out. It is shown that MLP exhibits the ''instantaneous'' learning effect and improves the quality of recognition in the case of input data represented in the form of variational series. The reasons are analyzed that underlie these effects. Recommendations for joint usage of the omega(n)(k) criteria and of MLPs are given.
goodness-of-fit criteria variational series multivariate classifiers neural network