Generally,the performance of deep learning models is related to the captured features of training samples.When the training samples belong to different domains,the diverse features may increase the difficulty of training high performance models.In this paper,we built a new framework that generates multiple models on the organized samples to increase the accuracy of classification.Firstly,our framework selects some existing models and trains each of them on organized training sets to get multiple trained models.Secondly,we select some of them based on a validation set.Finally,we use some fusion method on the outputs of the selected models to get more accurate results.The experimental results show that our framework achieved higher accuracy than the existing methods.Our framework can be an option for the deep learning system to increase the classification accuracy.
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机译:Comments on, Xuan Li, Shanghong Zhao, Zihang Zhu, Bing Gong, Xingchun Chu, Yongjun Li, Jing Zhao and Yun Liu 'an optical millimeter-wave generation scheme based on two parallel dual-parallel Mach-Zehnder modulators and polarization multiplexing', Journal of Modern Optics, 2015