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The deep multiple kernel learning (DMKL) method has caused widespread concern due to its better results compared with shallow multiple kernel learning. However, existing DMKL methods, which have a fixed number of layers and fixed type of kernels, have poor ability to adapt to different data sets and are difficult to find suitable model parameters to improve the test accuracy. In this paper, we propose a self-adaptive deep multiple kernel learning (SA-DMKL) method. Our SA-DMKL method can adaptdoi:10.3390/sym11030325 fatcat:my2oztokfbhflmofecqfylluwq