Azarbaijan Shahid Madani University Communications in Combinatorics and Optimization 2538-2128 10 2 2025 06 01 A hybrid conjugate gradient method between MLS and FR in nonparametric statistics 405 421 14667 10.22049/cco.2023.28935.1784 EN Imane Guefassa Laboratory Informatics and Mathematics, Mohamed Cherif Messaadia University, Souk Ahras, 41000, Algeria Yacine Chaib Laboratory Informatics and Mathematics, Mohamed Cherif Messaadia University, Souk Ahras, 41000, Algeria Tahar Bechouat Mohamed Cherif Messaadia University, Souk Ahras, 41000, Algeria Journal Article 2023 08 19 This paper proposes a novel hybrid conjugate gradient method for nonparametric statistical inference.The proposed method is a convex combination of the modified linear search (MLS) and Fletcher-Reeves (FR) methods, and it inherits the advantages of both methods. The FR method is known for its fast convergence, while the MLS method is known for its robustness to noise. The proposed method combines these advantages to achieve both fast convergence and robustness to noise. Our method is evaluated on a variety of nonparametric statistical problems, including kernel density estimation, regression, and classification. The results show that the new method outperforms the MLS and FR methods in terms of both accuracy and efficiency. https://comb-opt.azaruniv.ac.ir/article_14667_d95cb165afa3982341b00b0a436897cd.pdf