In this paper we investigate the benefit of augmenting data with
synthetically created samples when training a machine learning classifier. Two
approaches for creating additional training samples are data warping, which
generates additional samples through transformations applied in the data-space,
and synthetic over-sampling, which creates additional samples in feature-space.
We experimentally evaluate the benefits of data augmentation for a
convolutional backpropagation-trained neural network, a convolutional support
vector machine and a convolutional extreme learning machine classifier, using
the standard MNIST handwritten digit dataset. We found that while it is
possible to perform generic augmentation in feature-space, if plausible
transforms for the data are known then augmentation in data-space provides a
greater benefit for improving performance and reducing overfitting.

本文研究了在机器学习分类器的训练中，通过合成数据样本来增强数据的效果。通过数据扭曲和特征空间中的合成过采样两种方法，我们对卷积神经网络，卷积支持向量机和卷积极限学习机分类器在标准的 MNIST 手写数字数据集上进行了实验评估。实验结果表明，在数据空间中合成数据可以更有效地提高性能和缓解过拟合问题。