BottomHatTransform[image, ker] gives the morphological bottom-hat transform of image with respect to structuring element ker.BottomHatTransform[image, r] gives the bottom-hat ...
GeneralizedLinearModelFit[{y_1, y_2, ...}, {f_1, f_2, ...}, x] constructs a generalized linear model of the form g -1 (\[Beta]_0 + \[Beta]_1 f_1 + \[Beta]_2 f_2 + ...) that ...
InverseWaveletTransform[dwd] gives the inverse wavelet transform of a DiscreteWaveletData object dwd.InverseWaveletTransform[dwd, wave] gives the inverse transform using the ...
LinearModelFit[{y_1, y_2, ...}, {f_1, f_2, ...}, x] constructs a linear model of the form \[Beta]_0 + \[Beta]_1 f_1 + \[Beta]_2 f_2 + ... that fits the y_i for successive x ...
LogitModelFit[{y_1, y_2, ...}, {f_1, f_2, ...}, x] constructs a binomial logistic regression model of the form 1/(1 + E -(\[Beta]_0 + \[Beta]_1 f_1 + \[Beta]_2 f_2 + \ ...)) ...
MexicanHatWavelet[] represents the Mexican hat wavelet of width 1.MexicanHatWavelet[\[Sigma]] represents the Mexican hat wavelet of width \[Sigma].
NonlinearModelFit[{y_1, y_2, ...}, form, {\[Beta]_1, ...}, x] constructs a nonlinear model with structure form that fits the y_i for successive x values 1, 2, ... using the ...
ProbitModelFit[{y_1, y_2, ...}, {f_1, f_2, ...}, x] constructs a binomial probit regression model of the form 1/2 (1 + erf((\[Beta]_0 + \[Beta]_1 f_1 + \[Beta]_2 f_2 + \ ...
TopHatTransform[image, ker] gives the morphological top-hat transform of image with respect to structuring element ker.TopHatTransform[image, r] gives the top-hat transform ...
WaveletPsi[wave, x] gives the wavelet function \[Psi](x) for the symbolic wavelet wave evaluated at x.WaveletPsi[wave] gives the wavelet function as a pure function.