Qi Wang1, Zhaoying Liu1, Ting Zhang1,*, Shanshan Tu1, Yujian Li2, Muhammad Waqas3
Journal on Artificial Intelligence, Vol.4, No.1, pp. 37-48, 2022, DOI:10.32604/jai.2022.027875
Abstract Deep kernel mapping support vector machines have achieved good results in numerous tasks by mapping features from a low-dimensional space to a high-dimensional space and then using support vector machines for classification. However, the depth kernel mapping support vector machine does not take into account the connection of different dimensional spaces and increases the model parameters. To further improve the recognition capability of deep kernel mapping support vector machines while reducing the number of model parameters, this paper proposes a framework of Lightweight Deep Convolutional Cross-Connected Kernel Mapping Support Vector Machines (LC-CKMSVM). The framework consists of a feature extraction module… More >
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