@article{Liu2020, 
author = {Xinxin Liu and Yunfeng Zhang and Fangxun Bao and Kai Shao and Ziyi Sun and Caiming Zhang},
title = {Kernel-blending connection approximated by a neural network for image classification},
year = {2020},
journal = {Computational Visual Media},
volume = {6},
number = {4},
pages = {467-476},
keywords = {image classification, blending neural network, function approximation, kernel mapping connection, generalizability},
url = {https://www.sciopen.com/article/10.1007/s41095-020-0181-9},
doi = {10.1007/s41095-020-0181-9},
abstract = {This paper proposes a kernel-blending connection approximated by a neural network (KBNN) for image classification. A kernel mapping connection structure, guaranteed by the function approximation theorem, is devised to blend feature extraction and feature classification through neural network learning. First, a feature extractor learns features from the raw images. Next, an automatically constructed kernel mapping connection maps the feature vectors into a feature space. Finally, a linear classifier is used as an output layer of the neural network to provide classification results. Furthermore, a novel loss function involving a cross-entropy loss and a hinge loss is proposed to improve the generalizability of the neural network. Experimental results on three well-known image datasets illustrate that the proposed method has good classification accuracy and generalizability.}
}