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

Weite Li, Bo Zhou, Benhui Chen, Takayuki Furuzuki

研究成果: Article

9 引用 (Scopus)

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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.

元の言語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
発行部数12
DOI
出版物ステータスPublished - 2016 12 1

ASJC Scopus subject areas

  • Signal Processing
  • Computer Graphics and Computer-Aided Design
  • Applied Mathematics
  • Electrical and Electronic Engineering

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