Abstract
Ensemble learning, which combines multiple weak classifiers to form a strong classifier, has been shown to improve model accuracy and generalization. In the course of our research on convolutional neural networks (CNNs), we have discovered a novel model aggregation method called CoME, which accelerates model training speed, enhances model precision, and exhibits good interpretability. Starting from the relationship between convolutions and planar point classification problems, this paper explores the working mechanism of convolutions and their classification principles. We demonstrate how to leverage these principles to construct our ensemble model. Additionally, during the process of model simplification, we derive the Softmax function and gain new insights into the numerical values flowing through the model, referred to as logarithmic probability. Finally, we conduct experiments to validate the effectiveness of our model.
This research was funded by the National Natural Science Foundation of China, grant number 42172323.
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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Deng, L., Gao, X., Xiao, Y., Chang, S., Cheng, X., Yu, X. (2024). CoME: Collaborative Model Ensemble for Fast and Accurate Predictions. In: Sun, Y., Lu, T., Wang, T., Fan, H., Liu, D., Du, B. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2023. Communications in Computer and Information Science, vol 2013. Springer, Singapore. https://doi.org/10.1007/978-981-99-9640-7_16
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DOI: https://doi.org/10.1007/978-981-99-9640-7_16
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