SVM::predict is dominated by the per-feature kernel reduction over the
support vectors. Vectorize the four reduction kernels in svm.cpp with
universal intrinsics (two independent accumulators + scalar tail):
calc_non_rbf_base (dot product), calc_rbf (squared distance),
calc_intersec (min-sum) and calc_chi2. Same approach as the KNN
findNearest reduction in #29380. Adds modules/ml/perf/perf_svm.cpp
covering the RBF/POLY/INTER/CHI2 kernels.
Tonu Samuel