Boundary Sample Selection Method Based on Cosine Similarity

doi:10.3969/j.issn.1006-2475.2017.08.014

Abstract

Abstract:
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.

Key words: deep learning, convolutional neural network, pattern recognition, boundary data, image recognition, sample selection
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CLC Number:

TP301.6

LI Chunli， LIU Zhendong， HUI Kanghua. Boundary Sample Selection Method Based on Cosine Similarity[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2017.08.014.

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