Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
The optimal set features determination in discriminant analysis by the group method of data handling
Systems Analysis Modelling Simulation - Special issue on automatic model generation
Self-organising modelling as a part of simulation process
Systems Analysis Modelling Simulation - Special issue: Self-organising modelling and simulation
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It is shown analytically that the traditional scheme of sliding examination of the Group Method of Data Handling (GMDH) allows to solve the problem of discriminant analysis in a broad sense on the basis of finite samples of observations. It is shown that there exists an optimal set of features corresponding to the maximum mathematical expectation of some generalized distance between the observations from two general sets. It is shown analytically that parameters of the general sets and samples sizes influence on a complexity of optimal discriminant function, and conditions under which the optimal discriminant function is simplified are exhibited.