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Fast feedforward non-parametric deep learning network with automatic feature extraction

Angelov, Plamen, Gu, Xiaowei, Principe, Jose (2017) Fast feedforward non-parametric deep learning network with automatic feature extraction. In: 2017 International Joint Conference on Neural Networks (IJCNN). . pp. 534-541. IEEE ISBN 978-1-5090-6183-9. (doi:10.1109/IJCNN.2017.7965899) (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:90133)

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/IJCNN.2017.7965899

Abstract

In this paper, a new type of feedforward non-parametric deep learning network with automatic feature extraction is proposed. The proposed network is based on human-understandable local aggregations extracted directly from the images. There is no need for any feature selection and parameter tuning. The proposed network involves nonlinear transformation, segmentation operations to select the most distinctive features from the training images and builds RBF neurons based on them to perform classification with no weights to train. The design of the proposed network is very efficient (computation and time wise) and produces highly accurate classification results. Moreover, the training process is parallelizable, and the time consumption can be further reduced with more processors involved. Numerical examples demonstrate the high performance and very short training process of the proposed network for different applications.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/IJCNN.2017.7965899
Additional information: New member of staff, AAM requested 10.09.2021. Amy Boaler
Uncontrolled keywords: Feature extraction; Neurons; Training; Machine learning; Feedforward neural networks; Image segmentation; Computational efficiency; deep learning; fast training; feedforward; feature extraction; learning network
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 11:26 UTC
Last Modified: 01 Dec 2021 12:33 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90133 (The current URI for this page, for reference purposes)
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