BINGR: Binary Search based Gaussian Regression

Abstract

Regression is the study of functional dependency of one variable with respect to other variables. In this paper we propose a novel regression algorithm, BINGR, for predicting dependent variable, having the advantage of low computational complexity. The algorithm is interesting because instead of directly predicting the value of the response variable, it recursively narrows down the range in which response variable lies. BINGR reduces the computation order to logarithmic which is much better than that of existing standard algorithms. As BINGR is parameterless, it can be employed by any naive user. Our experimental study shows that our technique is as accurate as the state of the art, and faster by an order of magnitude. PROBLEM FORMULATIONIn this section, we present the mathematical model 258 Dubey H., Bharambe S.