High recall subtractive pattern recognition for image and other applications

A computer-implemented method, comprising:

• selecting a kernel and kernel parameters for a first Support Vector Machine (SVM) model comprising SVM support vector number of support vectors;
• testing the first SVM model on a feature matrix T to produce false positive (FP) data set and false negative (FN) data set by a computer system, wherein the feature matrix T includes n feature vectors of length m, wherein n and m are integer numbers;
• copying the feature matrix T to produce a feature matrix T_best comprising T_best sample number of sample points;
• checking, by the computer system, if a ratio (T_best sample number)/(SVM support vector number) is above a threshold for the first SVM model on T_best;
• and
• if the ratio is above the threshold, performing SVM predictions using the first SVM model on the feature matrix T_best.