Publication Date:
2016-03-09
Description:
The use of spline functions in the development of classification algorithms is examined. In particular, a method is formulated for producing spline approximations to bivariate density functions where the density function is decribed by a histogram of measurements. The resulting approximations are then incorporated into a Bayesiaan classification procedure for which the Bayes decision regions and the probability of misclassification is readily computed. Some preliminary numerical results are presented to illustrate the method.
Keywords:
NUMERICAL ANALYSIS
Type:
Proc. of the 2nd Ann. Symp. on Math. Pattern Recognition and Image Analysis Program; p 137-163
Format:
text
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