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  1. GO Loss: A Gaussian Distribution-Based Orthogonal Decomposition Loss for Classification.Mengxin Liu, Wenyuan Tao, Xiao Zhang, Yi Chen, Jie Li & Chung-Ming Own - 2019 - Complexity 2019:1-10.
    We present a novel loss function, namely, GO loss, for classification. Most of the existing methods, such as center loss and contrastive loss, dynamically determine the convergence direction of the sample features during the training process. By contrast, GO loss decomposes the convergence direction into two mutually orthogonal components, namely, tangential and radial directions, and conducts optimization on them separately. The two components theoretically affect the interclass separation and the intraclass compactness of the distribution of the sample features, respectively. Thus, (...)
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  • Effect of EOG Signal Filtering on the Removal of Ocular Artifacts and EEG-Based Brain-Computer Interface: A Comprehensive Study.Malik M. Naeem Mannan, M. Ahmad Kamran, Shinil Kang & Myung Yung Jeong - 2018 - Complexity 2018:1-18.
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  • Artificial Neural Network Classification of Motor-Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity.Vladimir A. Maksimenko, Semen A. Kurkin, Elena N. Pitsik, Vyacheslav Yu Musatov, Anastasia E. Runnova, Tatyana Yu Efremova, Alexander E. Hramov & Alexander N. Pisarchik - 2018 - Complexity 2018:1-10.
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