Subjects: Mathematics >> Computational Mathematics. submitted time 2018-04-03
Abstract: 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.
Peer Review Status:Awaiting Review
Subjects: Mathematics >> Computational Mathematics. submitted time 2018-03-22
Abstract: 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.
Peer Review Status:Awaiting Review