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PRTools User Guide



Trainable classifier based on Mixture of Gaussians

    W = MOGC(A,N)
    W = A*MOGC([],N,R,S);
    W = A*MOGC(N,R,S);

     A Dataset
     N Number of mixtures (optional; default 2)
     R,S Regularisation parameters, 0 <= R,S <= 1, see QDC


For each class j in A a density estimate is made by GAUSSM, using N(j)  mixture components. Using the class prior probabilities they are combined  into a single classifier W. If N is a scalar, this number is applied to  all classes. The relative size of the components is stored in W.DATA.PRIOR.

For small class sizes or for large values of N it may be difficult or  impossible to find the desired number of components. This may result in  relatively long computing times.



See also

datasets, mappings, qdc, plotm, testc,

PRTools Contents

PRTools User Guide

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