BINGR: Binary Search based Gaussian Regression

Harshit Dubey, Saket Bharambe, Vikram Pudi

2012

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.

Paper Citation

in Harvard Style

Dubey H., Bharambe S. and Pudi V. (2012). BINGR: Binary Search based Gaussian Regression . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012) ISBN 978-989-8565-29-7, pages 258-263. DOI: 10.5220/0004159302580263

in Bibtex Style

@conference{kdir12,
author={Harshit Dubey and Saket Bharambe and Vikram Pudi},
title={BINGR: Binary Search based Gaussian Regression},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)},
year={2012},
pages={258-263},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004159302580263},
isbn={978-989-8565-29-7},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)
TI - BINGR: Binary Search based Gaussian Regression
SN - 978-989-8565-29-7
AU - Dubey H.
AU - Bharambe S.
AU - Pudi V.
PY - 2012
SP - 258
EP - 263
DO - 10.5220/0004159302580263