Stochastic Systems

Investigation and optimisation of Rosenblatt–Parsen classifier with the aid of asymptotic expansions

Yu. S. Kharin

Minsk

Abstract: The paper is concerned with a learning classifier of multidimensional observations where Rosenblatt–Parsen estimates are used for observation probability distribution densities. An approximate expression is obtained for the risk (mean losses) of the classification as a function of training sample sizes. With the rist at its minimal, the optimal values of the classifier parameters (fuzziness coefficients) are found. Numerical examples are given.

UDC: 62-505:519.272


Received: 30.03.1981

 English version: Automation and Remote Control, 1983, 44:1, 72–80

Bibliographic databases:

•