Data-Independent Feature Learning with Markov Random Fields in Convolutional Neural Networks-Reference-Cited by-同舟云学术

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Data-Independent Feature Learning with Markov Random Fields in Convolutional Neural Networks

Published:2020-02 Issue: Volume:378 Page:24-35
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Peng Yao,Hankins Richard,Yin Hujun^ORCID

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference73 articles.

1. Representation learning: A review and new perspectives;Bengio;IEEE Trans. Pattern Anal. Mach. Intell.,2013

2. A fast learning algorithm for deep belief nets;Hinton;Neural Comput.,2006

3. Deep learning;LeCun;Nature,2015

4. What is the best multi-stage architecture for object recognition?;Jarrett,2009

5. Multi-column deep neural networks for image classification;Cireşan,2012

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1. Combining of Markov Random Field and Convolutional Neural Networks for Hyper/Multispectral Image Classification;Intelligent Data Engineering and Automated Learning – IDEAL 2023;2023

2. Research on feature extraction method of ship radiated noise with K-nearest neighbor mutual information variational mode decomposition, neural network estimation time entropy and self-organizing map neural network;Measurement;2022-08

3. Analysis and Optimization of Flute Playing and Teaching System Based on Convolutional Neural Network;Mathematical Problems in Engineering;2022-05-30

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