  Classification is one of the main areas of pattern recognition research, and
within it, Support Vector Machine (SVM) is one of the most popular methods
outside of field of deep learning -- and a de-facto reference for many Machine
Learning approaches. Its performance is determined by parameter selection,
which is usually achieved by a time-consuming grid search cross-validation
procedure (GSCV). That method, however relies on the availability and quality
of labelled examples and thus, when those are limited can be hindered. To
address that problem, there exist several unsupervised heuristics that take
advantage of the characteristics of the dataset for selecting parameters
instead of using class label information. While an order of magnitude faster,
they are scarcely used under the assumption that their results are
significantly worse than those of grid search. To challenge that assumption, we
have proposed improved heuristics for SVM parameter selection and tested it
against GSCV and state of the art heuristics on over 30 standard classification
datasets. The results show not only its advantage over state-of-art heuristics
but also that it is statistically no worse than GSCV.
