Regularization of neural network model with distance metric learning for i-vector based spoken language identification

2017 ◽  
Vol 44 ◽  
pp. 48-60 ◽  
Author(s):  
Xugang Lu ◽  
Peng Shen ◽  
Yu Tsao ◽  
Hisashi Kawai
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Metric Learning ◽  
Spoken Language ◽  
Language Identification ◽  
Distance Metric Learning ◽  
Distance Metric

scholarly journals Rare bioparticle detection via deep metric learning

RSC Advances ◽  
2021 ◽  
Vol 11 (29) ◽  
pp. 17603-17610
Author(s):  
Shaobo Luo ◽  
Yuzhi Shi ◽  
Lip Ket Chin ◽  
Yi Zhang ◽  
Bihan Wen ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Networks ◽  
Metric Learning ◽  
Practical Applications ◽  
Deep Metric Learning

Conventional deep neural networks use simple classifiers to obtain highly accurate results. However, they have limitations in practical applications. This study demonstrates a robust deep metric neural network model for rare bioparticle detection.

Neural Network Model to Calculate the Creep Data Using Size

2017 ◽  
Vol 7 (8) ◽  
pp. 257
Author(s):  
Seetharam .K ◽  
Sharana Basava Gowda ◽  
. Varadaraj
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Software Engineering ◽  
Network Model ◽  
Neural Network Model ◽  
Software Metrics ◽  
Creep Data ◽  
Engineering Software ◽  
Function Points ◽  
Different Levels

In Software engineering software metrics play wide and deeper scope. Many projects fail because of risks in software engineering development[1]t. Among various risk factors creeping is also one factor. The paper discusses approximate volume of creeping requirements that occur after the completion of the nominal requirements phase. This is using software size measured in function points at four different levels. The major risk factors are depending both directly and indirectly associated with software size of development. Hence It is possible to predict risk due to creeping cause using size.

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