Combining additive input noise annealing and pattern transformations for improved handwritten character recognition
作者：
Highlights：
• Handwritten digit recognition with very low error rates is a demanding problem.
• Traditional Back Propagation learning is limited due to local minima and stalling.
• There is also the need of building a full and representative learning data set.
• We address both problems with affine transformations and input noise annealing.
• Dimensionality reduction also helps to decrease the error rate.
摘要
•Handwritten digit recognition with very low error rates is a demanding problem.•Traditional Back Propagation learning is limited due to local minima and stalling.•There is also the need of building a full and representative learning data set.•We address both problems with affine transformations and input noise annealing.•Dimensionality reduction also helps to decrease the error rate.
论文关键词：Artificial Neural Networks,Back Propagation,MNIST,Handwritten text recognition
论文评审过程：Available online 18 July 2014.
论文官网地址：https://doi.org/10.1016/j.eswa.2014.07.016
