A Successive Quadratic Approximation Approach for Tuning Parameters in a Previously Proposed Regression Algorithm

Patrick Hosein; Kris Manohar; Ken Manohar

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Paper

A Successive Quadratic Approximation Approach for Tuning Parameters in a Previously Proposed Regression Algorithm

Topics: Data Science; Feature Selection; Predictive Modeling

In Proceedings of the 12th International Conference on Data Science, Technology and Applications DATA - Volume 1, 629-633, 2023 , Rome, Italy

Authors: Patrick Hosein ; Kris Manohar and Ken Manohar

Affiliation: Department of Computer Science, The University of the West Indies, St. Augustine, Trinidad

Keyword(s): Regression, Parameter Tuning, Convex Optimization, Machine Learning.

Abstract: We investigate a previously proposed regression algorithm that provides excellent performance but requires significant computing resources for parameter optimization. We summarize this previously proposed algorithm and introduce an efficient approach for parameter tuning. The speedup provided by this optimization approach is illustrated over a wide range of examples. This speedup in parameter tuning increases the practicability of the proposed regression algorithm.

CC BY-NC-ND 4.0

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Paper citation in several formats:

Hosein, P.; Manohar, K. and Manohar, K. (2023). A Successive Quadratic Approximation Approach for Tuning Parameters in a Previously Proposed Regression Algorithm. In Proceedings of the 12th International Conference on Data Science, Technology and Applications - DATA; ISBN 978-989-758-664-4; ISSN 2184-285X, SciTePress, pages 629-633. DOI: 10.5220/0012148900003541

@conference{data23,
author={Patrick Hosein. and Kris Manohar. and Ken Manohar.},
title={A Successive Quadratic Approximation Approach for Tuning Parameters in a Previously Proposed Regression Algorithm},
booktitle={Proceedings of the 12th International Conference on Data Science, Technology and Applications - DATA},
year={2023},
pages={629-633},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012148900003541},
isbn={978-989-758-664-4},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Data Science, Technology and Applications - DATA
TI - A Successive Quadratic Approximation Approach for Tuning Parameters in a Previously Proposed Regression Algorithm
SN - 978-989-758-664-4
IS - 2184-285X
AU - Hosein, P.
AU - Manohar, K.
AU - Manohar, K.
PY - 2023
SP - 629
EP - 633
DO - 10.5220/0012148900003541
PB - SciTePress