Self Organizing Maps, Pattern Recognition and Financial Crises

2011 ◽  
Author(s):  
Sylvain Barthelemy ◽  
Pascal Devaux ◽  
Francois Faure ◽  
Matthieu Pautonnier
Keyword(s):  
Pattern Recognition ◽  
Financial Crises ◽  
Self Organizing Maps ◽  
Self Organizing

Self-Organizing Map Application for Iris Recognition

2014 ◽  
Vol 3 (2) ◽  
pp. 10
Author(s):  
Anna Sedrak Hovakimyan ◽  
Siranush Gegham Sargsyan ◽  
Arshak Nazaryan
Keyword(s):  
Pattern Recognition ◽  
Problem Solving ◽  
Iris Recognition ◽  
Classification Problem ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
The Neural Network ◽  
Human Iris ◽  
Iris Patterns ◽  
Self Organizing

Human iris is  a good subject of biometrical identification, since  iris patterns are unique like fingerprints. Iris is well protected against damage, unlike fingerprints, which can be harder to recognize after years of certain types of manual labor.A problem of iris recognition is considered in the paper. In machine learning, pattern recognition is the assignment of a label to a given input value. Pattern classification is an example of pattern recognition: it attempts to assign each input value to one of a given set of classes. Nowadays various techniques are used for this purpose, and in particular artificial neural networks.For iris recognition problem solving  Kohenen Self Organizing Maps are suggested to use. The software for iris recognition is developed  which is customizable and allows to select the appropriate parameters of the neural network to obtain the most satisfactory results. The developed Self-Organizing Map Library of classes can be used for various kinds of object classification problem solving as well as for any problems suitable to solve with Self-Organizing Maps.

Spectral Pattern Recognition Using Self-Organizing MAPS

2004 ◽  
Vol 44 (3) ◽  
pp. 1056-1064 ◽  
Author(s):  
Barry K. Lavine ◽  
Charles E. Davidson ◽  
David J. Westover
Keyword(s):  
Pattern Recognition ◽  
Self Organizing Maps ◽  
Spectral Pattern ◽  
Self Organizing

Self-organizing maps for pattern recognition in design of alloys

2017 ◽  
Vol 32 (10) ◽  
pp. 1067-1074 ◽  
Author(s):  
Rajesh Jha ◽  
George S. Dulikravich ◽  
Nirupam Chakraborti ◽  
Min Fan ◽  
Justin Schwartz ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Self Organizing Maps ◽  
Self Organizing

scholarly journals No Neuron Left Behind: A genetic approach to higher precision topological mapping of self-organizing maps

2015 ◽  
Vol 5 (1) ◽  
pp. 1-12
Author(s):  
Chris Gorman ◽  
Clint Rogers ◽  
Iren Valova
Keyword(s):  
Pattern Recognition ◽  
Input Data ◽  
Topological Representation ◽  
Genetic Approach ◽  
Self Organizing Maps ◽  
Data Set ◽  
Running Time ◽  
Left Behind ◽  
Self Organizing

AbstractSelf-organizing maps are extremely useful in the field of pattern recognition. They become less useful, however, when neurons fail to activate during training. This phenomenon occurs when neurons are initialized in areas of non-input and are far enough away from the input data to never move toward the input. These neurons effectively misrepresent the data set. This results in, among other things, patterns becoming unrecognizable.We introduce an algorithm called No Neuron Left Behind to solve this problem.We show that our algorithm produces a more accurate topological representation of the input space.We also show that no neuron clusters form in areas of noninput and that mapping quality of the SOM increases drastically when our algorithm is implemented. Finally, the running time of NNLB is better or comparable to classic SOM without it.

scholarly journals Self-organizing maps as a chemometric tool for aromatic pattern recognition of soluble coffee

2012 ◽  
Vol 34 (1) ◽  
Author(s):  
Evandro Bona ◽  
Rui Sérgio dos Santos Ferreira da Silva ◽  
Dionísio Borsato ◽  
Denisley Gentil Bassoli
Keyword(s):  
Pattern Recognition ◽  
Self Organizing Maps ◽  
Soluble Coffee ◽  
Self Organizing
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