Supervised Machine Learning with a Novel Kernel Density Estimator

In recent years, kernel density estimation has been exploited by computer scientists to model machine learning problems. The kernel density estimation based approaches are of interest due to the low time complexity of either O(n) or O(n*log(n)) for constructing a classifier, where n is the number of sampling instances. Concerning design of kernel density estimators, one essential issue is how fast the pointwise mean square error (MSE) and/or the integrated mean square error (IMSE) diminish as the number of sampling instances increases. In this article, a novel kernel density estimator with the pointwise MSE converging at O(n^(-2/3)) for a specific class of distributions regardless of the dimension of the dataset is proposed.