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For example, quadratic classifier assuming Gaussian densities (qdc), returns class conditional densities as soft outputs.. In 2D feature spaces, we can visualize the classifiers
Alternatively, you can set the operating point in sdroc object manually (r=setcurop(r,30)) We can now estimate the confusion matrix on the test set:..
So, cmaps begins with data normalization by variance, then computes a Euclidean distance matrix which is fed into the classical scaling routine to derive a projected space in 15D. To
If the so-called base classifiers ( w1, w2,. .) do not output posterior probabilities, but for instance distances to the classifier boundaries, then these combining rules
>> c = spatm(b,2) % spatial mapping using smoothing of sigma = 2.0 16384 by 6 dataset with 1 class: [16384]. Let us visualise the resulting dataset c
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