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Our project comprises of analysis of the existing algorithms for
data compression so that we can utilize some features of these
algorithms use them to build a more efficient algorithm. We have
also considered detailed study to observe the efficiency of the
algorithms for different document types with the intention of
building a comprehensive data compression technique. We need to
study and analyze all the algorithms separately to incorporate the
positives from each of them and try to rectify the flaws that exist
in these algorithms. For clear elucidation of the data compression
concepts we try to discuss each standard algorithm in detail. There
is a close connection between machine learning and compression: a
system that predicts the posterior probabilities of a sequence
given its entire history can be used for optimal data compression
(by using arithmetic coding on the output distribution), while an
optimal compressor can be used for prediction (by finding the
symbol that compresses best, given the previous history). This
equivalence has been used as justification for data compression as
a benchmark for "general intelligence."
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