A deep neural network based quasi-linear kernel for support vector machines

Weite Li, Bo Zhou, Benhui Chen, Jinglu Hu

研究成果: Article査読

10 被引用数 (Scopus)

抄録

This paper proposes a deep quasi-linear kernel for support vector machines (SVMs). The deep quasi-linear kernel can be constructed by using a pre-trained deep neural network. To realize this goal, a multilayer gated bilinear classifier is first designed to mimic the functionality of the pre-trained deep neural network, by generating the gate control signals using the deep neural network. Then, a deep quasi-linear kernel is derived by applying an SVM formulation to the multilayer gated bilinear classifier. In this way, we are able to further implicitly optimize the parameters of the multilayer gated bilinear classifier, which are a set of duplicate but independent parameters of the pre-trained deep neural network, by using an SVM optimization. Experimental results on different data sets show that SVMs with the proposed deep quasi-linear kernel have an ability to take advantage of the pre-trained deep neural networks and outperform SVMs with RBF kernels.

本文言語English
ページ(範囲)2558-2565
ページ数8
ジャーナルIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
E99A
12
DOI
出版ステータスPublished - 2016 12

ASJC Scopus subject areas

  • 信号処理
  • コンピュータ グラフィックスおよびコンピュータ支援設計
  • 電子工学および電気工学
  • 応用数学

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