Image recognition using convolutional neural network combined with ensemble learning algorithm
| العنوان: | Image recognition using convolutional neural network combined with ensemble learning algorithm |
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| المؤلفون: | Xiaoshu Luo, Yexiu Zhong, Wenjie Jiang, Weilong Mo |
| المصدر: | Journal of Physics: Conference Series. 1237:022026 |
| بيانات النشر: | IOP Publishing, 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | History, business.industry, Computer science, Network structure, Training methods, Convolutional neural network, Ensemble learning, Computer Science Applications, Education, ComputingMethodologies_PATTERNRECOGNITION, Computer Science::Computer Vision and Pattern Recognition, Test set, Learning differences, Computer vision, Artificial intelligence, business, Classifier (UML), Algorithm |
| الوصف: | An image recognition algorithm based on ensemble learning algorithm and convolution neural network structure (ELA-CNN) is proposed to solve the problem that a single convolution neural network (CNN) classifier may be more prone to error or unreliable prediction. In order to improve the effect of ensemble learning, enhance the transfer of features, extract deeper features and multi-scale features, the network structure uses various model structure of the mainstream algorithms. Bagging training method is used in the training process, that is, different learners use different data sets to ensure the learning differences. Finally, the prediction result of all classifiers is used to get the final image target recognition according to the decision algorithm. The algorithm is simulated with of the open data set of cifar-10. The experimental results show that the proposed algorithm has a high recognition accuracy. The recognition rate of the test set reaches 98.89% and the recognition result is securer and reliable. |
| تدمد: | 1742-6596 1742-6588 |
| DOI: | 10.1088/1742-6596/1237/2/022026 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::f782d33b954ef8346759f490cf140e84 https://doi.org/10.1088/1742-6596/1237/2/022026 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi...........f782d33b954ef8346759f490cf140e84 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 17426596 17426588 |
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| DOI: | 10.1088/1742-6596/1237/2/022026 |