@inproceedings{7230051b160d456c91581e9779e1cc66,
title = "Improved SVM-AdaBoost Stacking Algorithm with ResNet18",
abstract = "Image classification with small amount of data has become a key issue in computer vision. In this paper, a novel stacking architecture based on support vector machine (SVM) and adaptive boosting (AdaBoost) is proposed. The 18-layer ResNet model is used to extract features, acting as the input of the stacking algorithm. Then, the SVM classifiers in the first layer produce the basic predictions, based on which, the AdaBoost classifier in the second layer decides the final class of image. Experimental results on CIFAR-10 dataset demonstrate that the SVM-AdaBoost stacking algorithm outperforms the existing competitive benchmark algorithms with single SVM or AdaBoost.",
keywords = "Adaptive Boosting, ResNet18, Stacking Algorithm, Support Vector Machine",
author = "Minxian Wei and Yuezu Lv and Jialing Zhou",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Unmanned Systems, ICUS 2021 ; Conference date: 15-10-2021 Through 17-10-2021",
year = "2021",
doi = "10.1109/ICUS52573.2021.9641106",
language = "English",
series = "Proceedings of 2021 IEEE International Conference on Unmanned Systems, ICUS 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "16--20",
booktitle = "Proceedings of 2021 IEEE International Conference on Unmanned Systems, ICUS 2021",
address = "United States",
}