TY - JOUR
AU - Tas, Engin
PY - 2022/10/30
Y2 - 2022/11/28
TI - Large Scale Ranking Using Stochastic Gradient Descent
JF - Proceedings of the Bulgarian Academy of Sciences
JA - C. R. Acad. Bulg. Sci.
VL - 75
IS - 10
SE - Mathematics
DO - 10.7546/CRABS.2022.10.03
UR - https://proceedings.bas.bg/index.php/cr/article/view/172
SP - 1419-1427
AB - <p>A system of linear equations can represent any ranking problem that minimizes a pairwise ranking loss. We utilize a fast version of gradient descent algorithm with a near-optimal learning rate and momentum factor to solve this linear equations system iteratively. Tikhonov regularization is also integrated into this framework to avoid overfitting problems where we have very large and high dimensional but sparse data. </p>
ER -