  With data sizes constantly expanding, and with classical machine learning
algorithms that analyze such data requiring larger and larger amounts of
computation time and storage space, the need to distribute computation and
memory requirements among several computers has become apparent. Although
substantial work has been done in developing distributed binary SVM algorithms
and multi-class SVM algorithms individually, the field of multi-class
distributed SVMs remains largely unexplored. This research proposes a novel
algorithm that implements the Support Vector Machine over a multi-class dataset
and is efficient in a distributed environment (here, Hadoop). The idea is to
divide the dataset into half recursively and thus compute the optimal Support
Vector Machine for this half during the training phase, much like a divide and
conquer approach. While testing, this structure has been effectively exploited
to significantly reduce the prediction time. Our algorithm has shown better
computation time during the prediction phase than the traditional sequential
SVM methods (One vs. One, One vs. Rest) and out-performs them as the size of
the dataset grows. This approach also classifies the data with higher accuracy
than the traditional multi-class algorithms.
