Image Classification Using Convolutional Neural Networks and Kernel Extreme Learning Machines
Zhuangzi Li, Xiaobin Zhu, Lei Wang, Peiyu Guo · 2018
We know that convolutional neural networks are good at learning invariant features, but not always optimal for classification. Contrarily, Kernel Extreme Learning Machines (KELMs) are good at approximating any target continuous function with extremely fast speed, but cannot learn complicated invariances. In this paper, we propose a novel image classification framework, in which KELM instead of Softmax function is adopted as a classifier in the convolutional neural network (CNN) architecture for promoting the performance of image classification. Experiments conducted on the publicly available datasets demonstrate the superior performance of the proposed method.