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  • NUMERICAL ANALYSIS  (2)
  • 1
    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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  • 2
    Publication Date: 2014-09-10
    Description: The use of spline functions in the development of classification algorithms is discussed. A method is formulated for producing spline approximations to univariate density functions when each density function is described by a histogram of measurements. The resulting approximations are then incorporated into a Bayesian classification procedure for which the probability of misclassification can be readily computed. Some preliminary numerical results are presented to illustrate the method.
    Keywords: NUMERICAL ANALYSIS
    Type: Proc. of the NASA Symp. on Math. Pattern Recognition and Image Analysis; p 167-190
    Format: text
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