Dynamic synapse neural networks with a Gauss–Newton learning method for speech processing

2002 ◽  
Vol 111 (5) ◽  
pp. 2478
Author(s):  
Hassan Heidari Namarvar ◽  
Alireza Afshordi Dibazar ◽  
Theodore W. Berger
Keyword(s):  
Neural Networks ◽  
Speech Processing ◽  
Learning Method ◽  
Dynamic Synapse

A Structure Learning Method for Block-Based Neural Networks

2013 ◽  
Vol 133 (10) ◽  
pp. 1976-1982 ◽  
Author(s):  
Hidetaka Watanabe ◽  
Seiichi Koakutsu ◽  
Takashi Okamoto ◽  
Hironori Hirata
Keyword(s):  
Neural Networks ◽  
Structure Learning ◽  
Learning Method ◽  
Block Based

An online supervised learning method for spiking neural networks with adaptive structure

2014 ◽  
Vol 144 ◽  
pp. 526-536 ◽  
Author(s):  
Jinling Wang ◽  
Ammar Belatreche ◽  
Liam Maguire ◽  
Thomas Martin McGinnity
Keyword(s):  
Neural Networks ◽  
Supervised Learning ◽  
Spiking Neural Networks ◽  
Learning Method ◽  
Adaptive Structure

scholarly journals Patch-Based Change Detection Method for SAR Images with Label Updating Strategy

2021 ◽  
Vol 13 (7) ◽  
pp. 1236
Author(s):  
Yuanjun Shu ◽  
Wei Li ◽  
Menglong Yang ◽  
Peng Cheng ◽  
Songchen Han
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Change Detection ◽  
Detection Method ◽  
Training Data ◽  
Synthetic Aperture ◽  
Learning Method ◽  
Sar Images ◽  
Two Stage ◽  
Second Stage

Convolutional neural networks (CNNs) have been widely used in change detection of synthetic aperture radar (SAR) images and have been proven to have better precision than traditional methods. A two-stage patch-based deep learning method with a label updating strategy is proposed in this paper. The initial label and mask are generated at the pre-classification stage. Then a two-stage updating strategy is applied to gradually recover changed areas. At the first stage, diversity of training data is gradually restored. The output of the designed CNN network is further processed to generate a new label and a new mask for the following learning iteration. As the diversity of data is ensured after the first stage, pixels within uncertain areas can be easily classified at the second stage. Experiment results on several representative datasets show the effectiveness of our proposed method compared with several existing competitive methods.

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