  One of the central problems in the classification of individual test
sequences (e.g. genetic analysis), is that of checking for the similarity of
sample test sequences as compared with a set of much longer training sequences.
This is done by a set of classifiers for test sequences of length N, where each
of the classifiers is trained by the training sequences so as to minimize the
classification error rate when fed with each of the training sequences.
  It should be noted that the storage of long training sequences is considered
to be a serious bottleneck in the next generation sequencing for Genome
analysis
  Some popular classification algorithms adopt a probabilistic approach, by
assuming that the sequences are realizations of some variable-length Markov
process or a hidden Markov process (HMM), thus enabling the imbeding of the
training data onto a variable-length Suffix-tree, the size of which is usually
linear in $N$, the length of the test sequence.
  Despite of the fact that it is not assumed here that the sequences are
realizations of probabilistic processes (an assumption that does not seem to be
fully justified when dealing with biological data), it is demonstrated that
"feature-based" classifiers, where particular substrings (called "features" or
markers) are sought in a set of "big data" training sequences may be based on a
universal compaction of the training data that is contained in a set of $t$
(long) individual training sequences, onto a suffix-tree with no more than O(N)
leaves, regardless of how long the training sequence is, at only a vanishing
increase in the classification error rate.
