A deep quasi-linear kernel composition method for support vector machines

Weite Li, Takayuki Furuzuki, Benhui Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In this paper, we introduce a data-dependent kernel called deep quasi-linear kernel, which can directly gain a profit from a pre-trained feedforward deep network. Firstly, a multi-layer gated bilinear classifier is formulated to mimic the functionality of a feed-forward neural network. The only difference between them is that the activation values of hidden units in the multi-layer gated bilinear classifier are dependent on a pre-trained neural network rather than a pre-defined activation function. Secondly, we demonstrate the equivalence between the multi-layer gated bilinear classifier and an SVM with a deep quasi-linear kernel. By deriving a kernel composition function, traditional optimization algorithms for a kernel SVM can be directly implemented to implicitly optimize the parameters of the multi-layer gated bilinear classifier. Experimental results on different data sets show that our proposed classifier obtains an ability to outperform both an SVM with a RBF kernel and the pre-trained feedforward deep network.

Original languageEnglish
Title of host publication2016 International Joint Conference on Neural Networks, IJCNN 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1639-1645
Number of pages7
Volume2016-October
ISBN (Electronic)9781509006199
DOIs
Publication statusPublished - 2016 Oct 31
Event2016 International Joint Conference on Neural Networks, IJCNN 2016 - Vancouver, Canada
Duration: 2016 Jul 242016 Jul 29

Other

Other2016 International Joint Conference on Neural Networks, IJCNN 2016
CountryCanada
CityVancouver
Period16/7/2416/7/29

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ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Li, W., Furuzuki, T., & Chen, B. (2016). A deep quasi-linear kernel composition method for support vector machines. In 2016 International Joint Conference on Neural Networks, IJCNN 2016 (Vol. 2016-October, pp. 1639-1645). [7727394] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2016.7727394