Scalable hybrid deep neural kernel networks

Siamak Mehrkanoon*, Andreas Zell, Johan A.K. Suykens

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

This paper introduces a novel hybrid deep neural kernel framework. The proposed deep learning model follows a combination of neural networks based architecture and a kernel based model. In partic- ular, here an explicit feature map, based on random Fourier features, is used to make the transition between the two architectures more straight- forward as well as making the model scalable to large datasets by solving the optimization problem in the primal. The introduced framework can be considered as the first building block for the development of even deeper models and more advanced architectures. Experimental results show a significant improvement over shallow models on several medium to large scale real-life datasets.

Original languageEnglish
Title of host publicationESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Publisheri6doc.com publication
Pages17-22
Number of pages6
ISBN (Electronic)9782875870391
Publication statusPublished - 2017
Event25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2017 - Bruges, Belgium
Duration: 26 Apr 201728 Apr 2017

Publication series

NameESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Conference

Conference25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2017
Country/TerritoryBelgium
CityBruges
Period26/04/1728/04/17

Bibliographical note

Funding Information:
The authors acknowledge support of ERC AdG A-DATADRIVE-B (290923), KUL: GOA/10/09 MaNet, CoE PFV/10/002 (OPTEC), BIL12/11T; FWO: G.0377.12, G.088114N, G0A4917N; IUAPP7/19 DYSCO.

Publisher Copyright:
© ESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. All rights reserved.

Funding

The authors acknowledge support of ERC AdG A-DATADRIVE-B (290923), KUL: GOA/10/09 MaNet, CoE PFV/10/002 (OPTEC), BIL12/11T; FWO: G.0377.12, G.088114N, G0A4917N; IUAPP7/19 DYSCO.

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