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Several network models including AlexNet, VGGNet, GoogLenet and ResNet, have achieved milestone contributions while relying on massive computing resources. However, when faced with a small number of labeled examples, especially in the case of unbalanced datasets, the cumulative error and time-consuming convergence reduce their efficacy. Inspired by the convolutional output layer with a [Formula: see text] kernel, a convolutional nonlinear transfer approach with partial cooperating (CNN-COL) is proposed to address this challenge. Meanwhile, a novel method for data augmented balance can enhance the influence of small and unbalanced samples in the CNN-COL. Related experiments show that the proposed CNN-COL can effectively improve the quality of a dataset and achieve superior performance with respect to traffic sign identification based on a small and type-unbalanced dataset. <\/jats:p>","DOI":"10.1142\/s0218843020400079","type":"journal-article","created":{"date-parts":[[2020,1,31]],"date-time":"2020-01-31T08:20:18Z","timestamp":1580458818000},"page":"2040007","source":"Crossref","is-referenced-by-count":1,"title":["Traffic Sign Identification Using a Partially Cooperative Strategy in a Convolutional Neural Network"],"prefix":"10.1142","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5408-7549","authenticated-orcid":false,"given":"Hongbo","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer and Communication Engineering, Beijing Key Lab of Knowledge Engineering for Materials Science, University of Science and Technology Beijing, Xueyuan Road 30, Haidian Zone, Beijing 100083, P. R. 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