逻辑回归上的反拉加速方法
he Counter Pull Acceleration Method for Logistic Regression
本文通过严格数学分析找出了逻辑回归过拟合的成因：边界样本的损失贡献比重大且随法向量增长而加速增大、边界样本分布散乱，顺便理清了正则项的作用机理。 利用过拟合机制，本文提出一种反拉方法，既能缓解过拟合，又能减少训练步数，在MNIST数据集上实现加速38.25倍，在CIFAR10数据集上实现加速5.61倍。
In this paper, I found the two reasons of overfitting of logistic regression: boundary samples occupy a larger and larger share as the length of normal vector becomes longer and longer, boundary samples do not fit their probability density function well. With the help of insight in overfitting, I propose a acceleration method for logistic regression and got a training speedup of 38.25 on MNIST dataset, a training speedup of 5.61 on CIFAR10 dataset.
计算技术、计算机技术
逻辑回归，过拟合解释，反拉加速
.逻辑回归上的反拉加速方法[EB/OL].(2018-03-22)[2025-08-18].https://chinaxiv.org/abs/201803.00428.点此复制
评论