Neural Network Based Inverse System Identification from Small Data Sets

2019 ◽  
Author(s):  
Chathura Wanigasekara ◽  
Akshya Swain ◽  
Sing Kiong Nguang ◽  
B. Gangadhara Prusty
Keyword(s):  
Neural Network ◽  
System Identification ◽  
Inverse System ◽  
Small Data ◽  
Data Sets ◽  
Small Data Sets

A Comparison Study of Mahalanobis-Taguchi System and Neural Network for Multivariate Pattern Recognition

2005 ◽  
Author(s):  
Jungeui Hong ◽  
Elizabeth A. Cudney ◽  
Genichi Taguchi ◽  
Rajesh Jugulum ◽  
Kioumars Paryani ◽  
...  
Keyword(s):  
Neural Network ◽  
Small Data ◽  
Data Sets ◽  
Comparison Study ◽  
Data Set ◽  
Set Size ◽  
Breast Cancer Study ◽  
Discriminant Ability ◽  
Small Data Sets ◽  
Multivariate Pattern

The Mahalanobis-Taguchi System is a diagnosis and predictive method for analyzing patterns in multivariate cases. The goal of this study is to compare the ability of the Mahalanobis-Taguchi System and a neural network to discriminate using small data sets. We examine the discriminant ability as a function of data set size using an application area where reliable data is publicly available. The study uses the Wisconsin Breast Cancer study with nine attributes and one class.

scholarly journals Method-Induced Errors in Fractal Analysis of Lung Microscopic Images Segmented with the Use of HistAENN (Histogram-Based Autoencoder Neural Network)

2018 ◽  
Vol 8 (12) ◽  
pp. 2356 ◽  
Author(s):  
Dorota Oszutowska-Mazurek ◽  
Przemyslaw Mazurek ◽  
Miroslaw Parafiniuk ◽  
Agnieszka Stachowicz
Keyword(s):  
Neural Network ◽  
Fractal Analysis ◽  
Human Error ◽  
Small Data ◽  
Data Sets ◽  
Hierarchy Level ◽  
Box Counting ◽  
Small Data Sets ◽  
Aided Diagnosis ◽  
Improve Patient

The designing of Computer-Aided Diagnosis (CADx) is necessary to improve patient condition analysis and reduce human error. HistAENN (Histogram-based Autoencoder Neural Network, the first hierarchy level) and the fractal-based estimator (the second hierarchy level) are assumed for segmentation and image analysis, respectively. The aim of the study is to investigate how to select or preselect algorithms at the second hierarchy level algorithm using small data sets and the semisupervised training principle. Method-induced errors are evaluated using the Monte Carlo test and an overlapping table is proposed for the rejection or tentative acceptance of particular segmentation and fractal analysis algorithms. This study uses lung histological slides and the results show that 2D box-counting substantially outweighs lacunarity for considered configurations. These findings also suggest that the proposed method is applicable for further designing of classification algorithms, which is essential for researchers, software developers, and forensic pathologist communities.

scholarly journals Using artificial neural networks to solve text classification problems

2019 ◽  
Author(s):  
Екатерина Попова ◽  
Ekaterina Popova ◽  
Владимир Спицын ◽  
Vladimir Spicyn ◽  
Юлия Иванова ◽  
...  
Keyword(s):  
Neural Network ◽  
Text Classification ◽  
Test Sample ◽  
Small Data ◽  
Data Sets ◽  
Classification Problems ◽  
Network Algorithms ◽  
The Neural Network ◽  
Starting Point ◽  
Small Data Sets

The article is devoted to neural network text classification algorithms. The relevance of this topic is due to the ever-growing volume of information on the Internet and the need to navigate it. In this paper, in addition to the classification algorithm, a description is also given of the methods of text preprocessing and vectorization, these steps are the starting point for most NLP tasks and make neural network algorithms efficient on small data sets. In the work, a sampling of 50,000 English IMDB movie reviews will be used as a dataset for training and testing the neural network. To solve this problem, an approach based on the use of a convolutional neural network was used. The maximum achieved accuracy for the test sample was 90.16%.

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