  An intrinsic problem of classifiers based on machine learning (ML) methods is
that their learning time grows as the size and complexity of the training
dataset increases. For this reason, it is important to have efficient
computational methods and algorithms that can be applied on large datasets,
such that it is still possible to complete the machine learning tasks in
reasonable time. In this context, we present in this paper a more accurate
simple process to speed up ML methods. An unsupervised clustering algorithm is
combined with Expectation, Maximization (EM) algorithm to develop an efficient
Hidden Markov Model (HMM) training. The idea of the proposed process consists
of two steps. In the first step, training instances with similar inputs are
clustered and a weight factor which represents the frequency of these instances
is assigned to each representative cluster. Dynamic Time Warping technique is
used as a dissimilarity function to cluster similar examples. In the second
step, all formulas in the classical HMM training algorithm (EM) associated with
the number of training instances are modified to include the weight factor in
appropriate terms. This process significantly accelerates HMM training while
maintaining the same initial, transition and emission probabilities matrixes as
those obtained with the classical HMM training algorithm. Accordingly, the
classification accuracy is preserved. Depending on the size of the training
set, speedups of up to 2200 times is possible when the size is about 100.000
instances. The proposed approach is not limited to training HMMs, but it can be
employed for a large variety of MLs methods.
