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Authors: Harshit Dubey ; Saket Bharambe and Vikram Pudi

Affiliation: International Institute of Information Technology - Hyderabad, India

Keyword(s): Regression, Gaussian, Prediction, Logarithmic Performance;, Linear Performance, Binary Search.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Clustering and Classification Methods ; Computational Intelligence ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Technologies ; Operational Research ; Optimization ; Pre-Processing and Post-Processing for Data Mining ; Soft Computing ; Symbolic Systems ; Web Mining

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.

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Paper citation in several formats:
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 - KDIR, (IC3K 2012) ISBN 978-989-8565-29-7; ISSN 2184-3228, pages 258-263. DOI: 10.5220/0004159302580263

@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 - KDIR, (IC3K 2012)},
year={2012},
pages={258-263},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004159302580263},
isbn={978-989-8565-29-7},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - KDIR, (IC3K 2012)
TI - BINGR: Binary Search based Gaussian Regression
SN - 978-989-8565-29-7
IS - 2184-3228
AU - Dubey, H.
AU - Bharambe, S.
AU - Pudi, V.
PY - 2012
SP - 258
EP - 263
DO - 10.5220/0004159302580263