Morphometric Shape Analysis using Learning Vector Quantization Neural Networks — An Example Distinguishing Two Microtine Vole Species

2011 ◽  
Vol 48 (6) ◽  
pp. 359-364 ◽  
Author(s):  
Valentijn van den Brink ◽  
Folmer Bokma
Keyword(s):  
Neural Networks ◽  
Vector Quantization ◽  
Shape Analysis ◽  
Learning Vector Quantization ◽  
Vole Species

scholarly journals Multipose Face Recognition-Based Combined Adaptive Deep Learning Vector Quantization

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Shahenda Sarhan ◽  
Aida A. Nasr ◽  
Mahmoud Y. Shams
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Face Recognition ◽  
Vector Quantization ◽  
Confusion Matrix ◽  
Recognition System ◽  
Learning Vector Quantization ◽  
Majority Voting ◽  
Support Vector ◽  
Reliability And Robustness

Multipose face recognition system is one of the recent challenges faced by the researchers interested in security applications. Different researches have been introduced discussing the accuracy improvement of multipose face recognition through enhancing the face detector as Viola-Jones, Real Adaboost, and Cascade Object Detector while others concentrated on the recognition systems as support vector machine and deep convolution neural networks. In this paper, a combined adaptive deep learning vector quantization (CADLVQ) classifier is proposed. The proposed classifier has boosted the weakness of the adaptive deep learning vector quantization classifiers through using the majority voting algorithm with the speeded up robust feature extractor. Experimental results indicate that, the proposed classifier provided promising results in terms of sensitivity, specificity, precision, and accuracy compared to recent approaches in deep learning, statistical, and classical neural networks. Finally, the comparison is empirically performed using confusion matrix to ensure the reliability and robustness of the proposed system compared to the state-of art.

Discrimination of wines based on 2D NMR spectra using learning vector quantization neural networks and partial least squares discriminant analysis

2006 ◽  
Vol 558 (1-2) ◽  
pp. 144-149 ◽  
Author(s):  
Saeed Masoum ◽  
Delphine Jouan-Rimbaud Bouveresse ◽  
Joseph Vercauteren ◽  
Mehdi Jalali-Heravi ◽  
Douglas Neil Rutledge
Keyword(s):  
Neural Networks ◽  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Vector Quantization ◽  
Nmr Spectra ◽  
2D Nmr ◽  
Learning Vector Quantization ◽  
2D Nmr Spectra

LVQ Neural Networks in Color Segmentation

2010 ◽  
pp. 45-63
Author(s):  
Erik Cuevas ◽  
Daniel Zaldivar ◽  
Marco Perez-Cisneros ◽  
Marco Block
Keyword(s):  
Neural Networks ◽  
Light Intensity ◽  
Vector Quantization ◽  
Learning Vector Quantization ◽  
Color Images ◽  
Color Segmentation ◽  
Pixel Classification ◽  
Input Space ◽  
Segmentation Algorithms ◽  
Image Pixels

Segmentation in color images is a complex and challenging task in particular to overcome changes in light intensity caused by noise and shadowing. Most of the segmentation algorithms do not tolerate variations in color hue corresponding to the same object. By means of the Learning Vector Quantization (LVQ) networks, neighboring neurons are able to learn how to recognize close sections of the input space. Neighboring neurons would thus correspond to color regions illuminated in different ways. This chapter presents an image segmentator approach based on LVQ networks which considers the segmentation process as a color-based pixel classification. The segmentator operates directly upon the image pixels using the classification properties of the LVQ networks. The algorithm is effectively applied to process sampled images showing its capacity to satisfactorily segment color despite remarkable illumination differences.

Workforce Assignment into Large-Sized Virtual Cells Using Learning Vector Quantization (LVQ) Approach

2012 ◽  
Vol 576 ◽  
pp. 705-709
Author(s):  
R.V. Murali
Keyword(s):  
Neural Networks ◽  
Vector Quantization ◽  
Manufacturing Systems ◽  
Cellular Manufacturing ◽  
Cell Formation ◽  
Learning Vector Quantization ◽  
Cellular Manufacturing Systems ◽  
Classification Rate ◽  
Worker Assignment ◽  
Virtual Cells

Much has been reported in literature on virtual cell formation problems while a limited work is reported on worker assignments. Virtual Cellular Manufacturing Systems (VCMS) have come into existence, replacing traditional Cellular Manufacturing Systems (CMS), to meet highly dynamic production conditions in terms of demand, production lots, processing times, product mix and production sequences. Although the problem of worker assignment and flexibility in cell based manufacturing environments has been studied and analyzed in plenty using various heuristics/mathematical models, application of Artificial Neural Networks (ANN), adapted from the biological neural networks, is the recent development in this field exploiting the ability of ANN to work out mathematically-difficult-to-solve problems. In this attempt, the previous work of the author has been further developed and an attempt has been made to apply Learning Vector Quantization (LVQ) approach into worker assignment problems for higher order virtual cells i.e., three cells configurations and analyze the suitability of LVQ approach in terms of successful classification rate and simulation parameters for a number of VCMS periods.

scholarly journals Analysis of the experimental positron lifetime spectra by neural networks

2003 ◽  
Vol 18 (1) ◽  
pp. 16-21 ◽  
Author(s):  
Senada Avdic ◽  
Roumiana Chakarova ◽  
Imre Pazsit
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Vector Quantization ◽  
Positron Lifetime ◽  
Learning Vector Quantization ◽  
Polymer Materials ◽  
Analysis Tool ◽  
Spectral Components ◽  
Artificial Neural

This paper deals with the analysis of experimental positron lifetime spectra in polymer materials by using various algorithms of neural networks. A method based on the use of artificial neural networks for unfolding the mean lifetime and intensity of the spectral components of simulated positron lifetime spectra was previously suggested and tested on simulated data [Pzzsitetal, Applied Surface Science, 149 (1998), 97]. In this work, the applicability of the method to the analysis of experimental positron spectra has been verified in the case of spectra from polymer materials with three components. It has been demonstrated that the backpropagation neural network can determine the spectral parameters with a high accuracy and perform the decomposi-tion of lifetimes which differ by 10% or more. The backpropagation network has not been suitable for the identification of both the parameters and the number of spectral components. Therefore, a separate artificial neural network module has been designed to solve the classification problem. Module types based on self-organizing map and learning vector quantization algorithms have been tested. The learning vector quantization algorithm was found to have better performance and reliability. A complete artificial neural network analysis tool of positron lifetime spectra has been constructed to include a spectra classification module and parameter evaluation modules for spectra with a different number of components. In this way, both flexibility and high resolution can be achieved.

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