  This article proposes a novel density estimation based algorithm for carrying
out supervised machine learning. The proposed algorithm features O(n) time
complexity for generating a classifier, where n is the number of sampling
instances in the training dataset. This feature is highly desirable in
contemporary applications that involve large and still growing databases. In
comparison with the kernel density estimation based approaches, the
mathe-matical fundamental behind the proposed algorithm is not based on the
assump-tion that the number of training instances approaches infinite. As a
result, a classifier generated with the proposed algorithm may deliver higher
prediction accuracy than the kernel density estimation based classifier in some
cases.
