A New Method to Assist Small Data Set Neural Network Learning

2006 ◽  
Author(s):  
Rongfu Mao ◽  
Haichao Zhu ◽  
Linke Zhang ◽  
Aizhi Chen
Keyword(s):  
Neural Network ◽  
New Method ◽  
Small Data ◽  
Data Set ◽  
Neural Network Learning ◽  
Network Learning ◽  
Small Data Set

scholarly journals Ambulatory and Laboratory Stress Detection Based on Raw Electrocardiogram Signals Using a Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4408 ◽  
Author(s):  
Hyun-Myung Cho ◽  
Heesu Park ◽  
Suh-Yeon Dong ◽  
Inchan Youn
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Mental Arithmetic ◽  
Small Data ◽  
Data Sets ◽  
Learning Method ◽  
Data Set ◽  
Electrocardiogram Signals ◽  
Proposed Model ◽  
Small Data Set

The goals of this study are the suggestion of a better classification method for detecting stressed states based on raw electrocardiogram (ECG) data and a method for training a deep neural network (DNN) with a smaller data set. We suggest an end-to-end architecture to detect stress using raw ECGs. The architecture consists of successive stages that contain convolutional layers. In this study, two kinds of data sets are used to train and validate the model: A driving data set and a mental arithmetic data set, which smaller than the driving data set. We apply a transfer learning method to train a model with a small data set. The proposed model shows better performance, based on receiver operating curves, than conventional methods. Compared with other DNN methods using raw ECGs, the proposed model improves the accuracy from 87.39% to 90.19%. The transfer learning method improves accuracy by 12.01% and 10.06% when 10 s and 60 s of ECG signals, respectively, are used in the model. In conclusion, our model outperforms previous models using raw ECGs from a small data set and, so, we believe that our model can significantly contribute to mobile healthcare for stress management in daily life.

Recognition of partial discharge of cable accessories based on convolutional neural network with small data set

2020 ◽  
Vol 39 (2) ◽  
pp. 431-446
Author(s):  
Anan Zhang ◽  
Jiahui He ◽  
Yu Lin ◽  
Qian Li ◽  
Wei Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Recognition Performance ◽  
Partial Discharge ◽  
Measure Data ◽  
Small Data ◽  
Data Set ◽  
Content Type ◽  
Fault Recognition ◽  
Small Data Set

Purpose Considering the problem that the high recognition rate of deep learning requires the support of mass data, this study aims to propose an insulating fault identification method based on small data set convolutional neural network (CNN). Design/methodology/approach Because of the chaotic characteristics of partial discharge (PD) signals, the equivalent transformation of the PD signal of unit power frequency period is carried out by phase space reconstruction to derive the chaotic features. At the same time, geometric, fractal, entropy and time domain features are extracted to increase the volume of feature data. Finally, the combined features are constructed and imported into CNN to complete PD recognition. Findings The results of the case study show that the proposed method can realize the PD recognition of small data set and make up for the shortcomings of the methods based on CNN. Also, the 1-CNN built in this paper has better recognition performance for four typical insulation faults of cable accessories. The recognition performance is improved by 4.37% and 1.25%, respectively, compared with similar methods based on support vector machine and BPNN. Originality/value In this paper, a method of insulation fault recognition based on CNN with small data set is proposed, which can solve the difficulty to realize insulation fault recognition of cable accessories and deep data mining because of insufficient measure data.

scholarly journals Lifted Relational Neural Networks: Efficient Learning of Latent Relational Structures

2018 ◽  
Vol 62 ◽  
pp. 69-100 ◽  
Author(s):  
Gustav Sourek ◽  
Vojtech Aschenbrenner ◽  
Filip Zelezny ◽  
Steven Schockaert ◽  
Ondrej Kuzelka
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Expressive Power ◽  
Stochastic Gradient Descent ◽  
Data Set ◽  
Neural Network Learning ◽  
Network Learning ◽  
First Order ◽  
Relational Structures ◽  
Efficient Learning

We propose a method to combine the interpretability and expressive power of firstorder logic with the effectiveness of neural network learning. In particular, we introduce a lifted framework in which first-order rules are used to describe the structure of a given problem setting. These rules are then used as a template for constructing a number of neural networks, one for each training and testing example. As the different networks corresponding to different examples share their weights, these weights can be efficiently learned using stochastic gradient descent. Our framework provides a flexible way for implementing and combining a wide variety of modelling constructs. In particular, the use of first-order logic allows for a declarative specification of latent relational structures, which can then be efficiently discovered in a given data set using neural network learning. Experiments on 78 relational learning benchmarks clearly demonstrate the effectiveness of the framework.

scholarly journals Reliable Early Breast Cancer Detection using Artificial Neural Network for Small Data Set

2021 ◽  
Vol 1755 (1) ◽  
pp. 012037
Author(s):  
V. Vijayasarveswari ◽  
M. Jusoh ◽  
T. Sabapathy ◽  
R.A.A. Raof ◽  
S. Khatun ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Artificial Neural Network ◽  
Early Breast Cancer ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Small Data ◽  
Data Set ◽  
Small Data Set ◽  
Artificial Neural

Speed up Neural Network Learning by GPGPU

2011 ◽  
Vol 131 (11) ◽  
pp. 1889-1894
Author(s):  
Yuta Tsuchida ◽  
Michifumi Yoshioka
Keyword(s):  
Neural Network ◽  
Neural Network Learning ◽  
Network Learning ◽  
Speed Up

The Lyapunov Exponent Variance of an Electronic Manufacturing Enterprise’s Daily Qualified Rate Time Series by Improved Small Data Sets Approach

2012 ◽  
Vol 197 ◽  
pp. 271-277
Author(s):  
Zhu Ping Gong
Keyword(s):  
Time Series ◽  
Lyapunov Exponent ◽  
Chaotic Time Series ◽  
Quality System ◽  
Quality Level ◽  
Largest Lyapunov Exponent ◽  
Small Data ◽  
Data Set ◽  
Electronic Manufacturing ◽  
Small Data Set

Small data set approach is used for the estimation of Largest Lyapunov Exponent (LLE). Primarily, the mean period drawback of Small data set was corrected. On this base, the LLEs of daily qualified rate time series of HZ, an electronic manufacturing enterprise, were estimated and all positive LLEs were taken which indicate that this time series is a chaotic time series and the corresponding produce process is a chaotic process. The variance of the LLEs revealed the struggle between the divergence nature of quality system and quality control effort. LLEs showed sharp increase in getting worse quality level coincide with the company shutdown. HZ’s daily qualified rate, a chaotic time series, shows us the predictable nature of quality system in a short-run.

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