Publication Date:
2019-07-13
Description:
An algorithm is developed for a learning, adaptive, statistical pattern classifier for remotely sensed data. The estimation procedure consists of two steps: (1) an optimal stochastic approximation of the parameters of interest, and (2) a projection of the parameters in time and space. The results reported are for Gaussian data in which the mean vector of each class may vary with time or position after the classifier is trained.
Keywords:
CYBERNETICS
Type:
Symposium on Machine Processing of Remotely Sensed Data; Jun 03, 1975 - Jun 05, 1975; West Lafayette, IN
Format:
text
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