Showing papers on "Feature vector published in 1975"
33 citations
01 Jan 1975
TL;DR: The subject of pattern recognition can be divided into two main areas of study: feature selection and classifier design, as summarized in Fig. 10.1.
Abstract: The subject of pattern recognition can be divided into two main areas of study: (1) feature selection and (2) classifier design, as summarized in Fig. 10.1. x(t) is a signal that belongs to K classes denoted by C 1 , C 2 ,..., C K .
6 citations
01 Jan 1975
TL;DR: The technique produces a single linear combination of the original measurements which minimizes the one-dimensional probability of misclassification defined by the transformed densities.
Abstract: The computational procedure and associated computer program for a linear feature selection technique are presented. The technique assumes that: a finite number, m, of classes exists; each class is described by an n-dimensional multivariate normal density function of its measurement vectors; the mean vector and covariance matrix for each density function are known (or can be estimated); and the a priori probability for each class is known. The technique produces a single linear combination of the original measurements which minimizes the one-dimensional probability of misclassification defined by the transformed densities.
2 citations
TL;DR: By storing training samples, where a sample is a feature vector, the class conditional probability density is estimated and the procedure is applied to surgical patient features, the features corresponding to the kind of operation, diagnosis, patients' age, patient's sex, etc.
2 citations