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A cascade of deep learning fuzzy rule-based image classifier and SVM

Angelov, Plamen, Gu, Xiaowei (2017) A cascade of deep learning fuzzy rule-based image classifier and SVM. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC). . pp. 746-751. IEEE ISBN 978-1-5386-1646-8. (doi:10.1109/SMC.2017.8122697) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:90129)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://doi.org/10.1109/SMC.2017.8122697

Abstract

In this paper, a fast, transparent, self-evolving, deep learning fuzzy rule-based (DLFRB) image classifier is proposed. This new classifier is a cascade of the recently introduced DLFRB classifier called MICE and an auxiliary SVM. The DLFRB classifier serves as the main engine and can identify a number of human interpretable fuzzy rules through a very short, transparent, highly parallelizable training process. The SVM based auxiliary plays the role of a conflict resolver when the DLFRB classifier produces two highly confident labels for a single image. Only the fundamental image transformation techniques (rotation, scaling and segmentation) and feature descriptors (GIST and HOG) are used for pre-processing and feature extraction, but the proposed approach significantly outperforms the state-of-art methods in terms of both time and precision. Numerical experiments based on a handwritten digits recognition problem are used to demonstrate the highly accurate and repeatable performance of the proposed approach.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/SMC.2017.8122697
Uncontrolled keywords: Support vector machines; Training; Feature extraction; Mice; Engines; Image resolution; Machine learning; deep learning; cascade classifiers; fuzzy rule-based classifier; SVM; handwritten digits recognition
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Amy Boaler
Date Deposited: 10 Sep 2021 10:23 UTC
Last Modified: 13 Sep 2021 10:50 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90129 (The current URI for this page, for reference purposes)
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