基于余弦相似度的边界样本选择方法

卷积神经网络模型的训练通常需要大量的训练样本，导致训练时间过长。针对这一问题，本文提出一种基于余弦相似度的边界样本选择方法，选取边界样本构造训练集。通过该方法分别对MNIST，CIFAR10，SVHN数据集进行样本选择，利用卷积神经网络分类器进行实验研究。实验结果表明：该方法能够保留训练集中的典型样本，剔除冗余样本，从而减少训练样本的数量，缩短网络训练时间，提高网络学习效率。

Boundary Sample Selection Method Based on Cosine Similarity

Abstract: The training of convolution neural network usually requires a lot of training samples, which causes the training time be too long. To solve this problem, this paper presents a boundary sample selection method based on cosine similarity. We select boundary samples as the training set of convolution neural network, and carry out example selection experiment on the MNIST, CIFAR10 and SVHN data sets. Then a convolutional neural network is used to carry out experiments. Experimental results show that this method can preserve the typical samples in the training set and eliminate redundant samples. Thereby, the number of training samples is reduced, the network training time is shortened and the learning efficiency of network is improved.