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Tonu Samuel
a015333f04
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.