Iterative Information Granulation for Novelty Detection in Complex Datasets

Rubio-Solis, A. and Panoutsos, G. (2016) Iterative Information Granulation for Novelty Detection in Complex Datasets. In: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). 2016 IEEE World Congress on Computational Intelligence, 24/07/2016-29/07/2016, Vancouver, Canada. Institute of Electrical and Electronics Engineers . ISBN 978-1-5090-0626-7

Abstract

Metadata

Authors/Creators:
  • Rubio-Solis, A.
  • Panoutsos, G.
Copyright, Publisher and Additional Information: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy.
Keywords: Detectors; Iterative methods; MIMICs; Indexes; Neural networks; Prototypes; Cognition
Dates:
  • Published: 10 November 2016
  • Published (online): 10 November 2016
  • Accepted: 1 April 2016
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Funding Information:
FunderGrant number
EUROPEAN COMMISSION - HORIZON 2020COMBILASER - 636902
INNOVATE UK (TSB)101947 / 41205-293373
Depositing User: Symplectic Sheffield
Date Deposited: 19 May 2017 13:56
Last Modified: 21 Mar 2018 04:50
Published Version: https://doi.org/10.1109/FUZZ-IEEE.2016.7737791
Status: Published
Publisher: Institute of Electrical and Electronics Engineers
Refereed: Yes
Identification Number: https://doi.org/10.1109/FUZZ-IEEE.2016.7737791

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