We observed that the performance of the Boost method continuously improved with the number of iterations T and with the number of samples S, but saturated at about 15-20 samples without visible over-fitting in the tested range of T .
we arbitrarily stopped training after obtaining 5, 000 features per sample, and used 35 samples for uni-gram version and 65 samples for the combined bi-gram version, resulting in models with 104K and 172K unique features, respectively.