Supervized Change Detection for SAR Imagery Based on Processing of a Low Size Training Data Set by an Ensemble of Self-Organizing Maps

2018 ◽  
Author(s):  
Victor-Emil Neagoe ◽  
Adrian-Dumitru Ciotec
Keyword(s):  
Change Detection ◽  
Training Data ◽  
Self Organizing Maps ◽  
Data Set ◽  
Sar Imagery ◽  
Self Organizing

Machine Learning: Automated Knowledge Acquisition Based on Unsupervised Neural Network and Expert System Paradigms

2005 ◽  
Vol 9 (6) ◽  
pp. 693-697 ◽  
Author(s):  
Nazar Elfadil ◽  
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Knowledge Acquisition ◽  
Training Data ◽  
Neural Network Models ◽  
Self Organizing Maps ◽  
Data Set ◽  
Unsupervised Neural Network ◽  
Automated Knowledge ◽  
Self Organizing

Self-organizing maps are unsupervised neural network models that lend themselves to the cluster analysis of high-dimensional input data. Interpreting a trained map is difficult because features responsible for specific cluster assignment are not evident from resulting map representation. This paper presents an approach to automated knowledge acquisition using Kohonen's self-organizing maps and k-means clustering. To demonstrate the architecture and validation, a data set representing animal world has been used as the training data set. The verification of the produced knowledge base is done by using conventional expert system.

Constraining self-organizing map facies analysis with stratigraphy: An approach to increase the credibility in automatic seismic facies classification

2017 ◽  
Vol 5 (2) ◽  
pp. T163-T171 ◽  
Author(s):  
Tao Zhao ◽  
Fangyu Li ◽  
Kurt J. Marfurt
Keyword(s):  
Facies Analysis ◽  
Vertical Axis ◽  
Training Data ◽  
Seismic Facies ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Data Set ◽  
Statistical Correlations ◽  
Facies Classification ◽  
Self Organizing

Pattern recognition-based seismic facies analysis techniques are commonly used in modern quantitative seismic interpretation. However, interpreters often treat techniques such as artificial neural networks and self-organizing maps (SOMs) as a “black box” that somehow correlates a suite of attributes to a desired geomorphological or geomechanical facies. Even when the statistical correlations are good, the inability to explain such correlations through principles of geology or physics results in suspicion of the results. The most common multiattribute facies analysis begins by correlating a suite of candidate attributes to a desired output, keeping those that correlate best for subsequent analysis. The analysis then takes place in attribute space rather than ([Formula: see text], [Formula: see text], and [Formula: see text]) space, removing spatial trends often observed by interpreters. We add a stratigraphy layering component to a SOM model that attempts to preserve the intersample relation along the vertical axis. Specifically, we use a mode decomposition algorithm to capture the sedimentary cycle pattern as an “attribute.” If we correlate this attribute to the training data, it will favor SOM facies maps that follow stratigraphy. We apply this workflow to a Barnett Shale data set and find that the constrained SOM facies map shows layers that are easily overlooked on traditional unconstrained SOM facies map.

Self-Organizing Map Convergence

2018 ◽  
Vol 9 (2) ◽  
pp. 61-84 ◽  
Author(s):  
Robert Tatoian ◽  
Lutz Hamel
Keyword(s):  
Quality Measures ◽  
Training Data ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Data Set ◽  
Clustering Problem ◽  
Neighborhood Function ◽  
Real World Datasets ◽  
Clustering Problems ◽  
Self Organizing

Self-organizing maps are artificial neural networks designed for unsupervised machine learning. Here in this article, the authors introduce a new quality measure called the convergence index. The convergence index is a linear combination of map embedding accuracy and estimated topographic accuracy and since it reports a single statistically meaningful number it is perhaps more intuitive to use than other quality measures. The convergence index in the context of clustering problems was proposed by Ultsch as part of his fundamental clustering problem suite as well as real world datasets. First demonstrated is that the convergence index captures the notion that a SOM has learned the multivariate distribution of a training data set by looking at the convergence of the marginals. The convergence index is then used to study the convergence of SOMs with respect to the different parameters that govern self-organizing map learning. One result is that the constant neighborhood function produces better self-organizing map models than the popular Gaussian neighborhood function.

Clustering of Zika viruses originating from different geographical regions using computational sequence descriptors

2019 ◽  
Vol 16 ◽  
Author(s):  
Marjan Vračko ◽  
Subhash C. Basak ◽  
Dwaipayan Sen ◽  
Ashesh Nandy
Keyword(s):  
Zika Virus ◽  
Virus Genome ◽  
Host Cells ◽  
Genome Sequences ◽  
Region Of Origin ◽  
Self Organizing Maps ◽  
Data Set ◽  
Mahalanobis Distances ◽  
Geographical Regions ◽  
Self Organizing

: In this report we consider a data set, which consists of 310 Zika virus genome sequences taken from different continents, Africa, Asia and South America. The sequences, which were compiled from GenBank, were derived from the host cells of different mammalian species (Simiiformes, Aedes opok, Aedes africanus, Aedes luteocephalus, Aedes dalzieli, Aedes aegypti, and Homo sapiens). For chemometrical treatment the sequences have been represented by sequence descriptors derived from their graphs or neighborhood matrices. The set was analyzed with three chemometrical methods: Mahalanobis distances, principal component analysis (PCA) and self organizing maps (SOM). A good separation of samples with respect to the region of origin was observed using these three methods. Background: Study of 310 Zika virus genome sequences from different continents. Objective: To characterize and compare Zika virus sequences from around the world using alignment-free sequence comparison and chemometrical methods. Method: Mahalanobis distance analysis, self organizing maps, principal components were used to carry out the chemometrical analyses of the Zika sequence data. Results: Genome sequences are clustered with respect to the region of origin (continent, country) Conclusion: Africa samples are well separated from Asian and South American ones.

scholarly journals SOMDNCD: Image Change Detection Based on Self-Organizing Maps and Deep Neural Networks

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 35915-35925 ◽  
Author(s):  
Ruliang Xiao ◽  
Runxi Cui ◽  
Mingwei Lin ◽  
Lifei Chen ◽  
Youcong Ni ◽  
...  
Keyword(s):  
Neural Networks ◽  
Change Detection ◽  
Deep Neural Networks ◽  
Self Organizing Maps ◽  
Image Change Detection ◽  
Self Organizing
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