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

Research output: Contribution to conference - Without ISBN/ISSN Conference paper

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Standard

Fast feedforward non-parametric deep learning network with automatic feature extraction. / Angelov, Plamen Parvanov; Gu, Xiaowei; Principe, Jose .

2017. 534-541 Paper presented at International Joint Conference on Neural Networks (IJCNN), .

Research output: Contribution to conference - Without ISBN/ISSN Conference paper

Harvard

Angelov, PP, Gu, X & Principe, J 2017, 'Fast feedforward non-parametric deep learning network with automatic feature extraction' Paper presented at International Joint Conference on Neural Networks (IJCNN), 14/05/17 - 19/05/17, pp. 534-541.

APA

Angelov, P. P., Gu, X., & Principe, J. (2017). Fast feedforward non-parametric deep learning network with automatic feature extraction. 534-541. Paper presented at International Joint Conference on Neural Networks (IJCNN), .

Vancouver

Angelov PP, Gu X, Principe J. Fast feedforward non-parametric deep learning network with automatic feature extraction. 2017. Paper presented at International Joint Conference on Neural Networks (IJCNN), .

Author

Angelov, Plamen Parvanov ; Gu, Xiaowei ; Principe, Jose . / Fast feedforward non-parametric deep learning network with automatic feature extraction. Paper presented at International Joint Conference on Neural Networks (IJCNN), .8 p.

Bibtex

@conference{eff7f1829034411d9062ddf5cf08bae7,
title = "Fast feedforward non-parametric deep learning network with automatic feature extraction",
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.",
author = "Angelov, {Plamen Parvanov} and Xiaowei Gu and Jose Principe",
year = "2017",
month = "5",
day = "14",
language = "English",
pages = "534--541",
note = "International Joint Conference on Neural Networks (IJCNN) ; Conference date: 14-05-2017 Through 19-05-2017",

}

RIS

TY - CONF

T1 - Fast feedforward non-parametric deep learning network with automatic feature extraction

AU - Angelov, Plamen Parvanov

AU - Gu, Xiaowei

AU - Principe, Jose

PY - 2017/5/14

Y1 - 2017/5/14

N2 - 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.

AB - 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.

M3 - Conference paper

SP - 534

EP - 541

ER -