  Motivated by real-world machine learning applications, we consider a
statistical classification task in a sequential setting where test samples
arrive sequentially. In addition, the generating distributions are unknown and
only a set of empirically sampled sequences are available to a decision maker.
The decision maker is tasked to classify a test sequence which is known to be
generated according to either one of the distributions. In particular, for the
binary case, the decision maker wishes to perform the classification task with
minimum number of the test samples, so, at each step, she declares that either
hypothesis 1 is true, hypothesis 2 is true, or she requests for an additional
test sample. We propose a classifier and analyze the type-I and type-II error
probabilities. We demonstrate the significant advantage of our sequential
scheme compared to an existing non-sequential classifier proposed by Gutman.
Finally, we extend our setup and results to the multi-class classification
scenario and again demonstrate that the variable-length nature of the problem
affords significant advantages as one can achieve the same set of exponents as
Gutman's fixed-length setting but without having the rejection option.
