RESUMO
In this paper, an approach to unsupervised pattern classifiation is discussed. The classification scheme is based on an approximation of the probability densities of each class under the assumption that the input patterns are of a normal mixture. The proposed technique for identifying the mixture does not require prior information. The description of the mixture in terms of convexity allows to determine, from a totally unlabeled set of samples, the number of components and, for each of them, approximate values of the mean vector, the covariance matrix, and the a priori probability. Discriminant functions can then be constructed. Computer simulations show that the procedure yields decision rules whose performances remain close to the optimum Bayes minimum error-rate, while involving only a small amount of computation.
RESUMO
This paper is based on the utilization of the very elementary principle of linear regression used in a recursive way. This technique tested on electrophysiological signals readily leads to the conception of a monitoring system built on a biprocessor unit. In a clinical context, the use of microprocessors leads then to the design of very compact devices including the capability of distributed processing which embrances the concept of intelligent monitoring. Finally, a proposal is given for the realization of a complete monitoring control desk (MCD) devoted to the survey of eight patients.
