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The SVM implementations described involve SVM algorithmic variants, kernel variants, and chunking variants; as well as SVM classification tuning metaheuristics; and SVM clustering metaheuristics. The SVM tuning metaheuristics typically enable use of the SVM’s confidence parameter to bootstrap from a strong classification engine to a strong clustering engine via use of label changes, and repeated SVM training processes with the new label information obtained.

SVM Methods and Systems are given in ssss1 for classification, clustering, and SSA in general, with a broad range of applications:

 sequential‐structure identification

 pattern recognition

 knowledge discovery

 bioinformatics

 nanopore detector cheminformatics

 computational engineering with information flows

 “SSA” Architectures favoring Deep Learning (see next section)

SVM binary discrimination outperforms other classification methods with or without dropping weak data (while many other methods cannot even identify weak data).