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Self-organizing maps and galaxy evol...

Beland, Jacques.

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Self-organizing maps and galaxy evolution.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Self-organizing maps and galaxy evolution./
Author:	Beland, Jacques.
Description:	138 p.
Notes:	Source: Masters Abstracts International, Volume: 54-01.
Contained By:	Masters Abstracts International54-01(E).
Subject:	Mathematics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1568591
ISBN:	9781321318166

Self-organizing maps and galaxy evolution.
Beland, Jacques.

Self-organizing maps and galaxy evolution. - 138 p.

Source: Masters Abstracts International, Volume: 54-01.

Thesis (M.S.)--Trent University (Canada), 2015.

Artificial Neural Networks (ANN) have been applied to many areas of research. These techniques use a series of object attributes and can be trained to recognize different classes of objects. The Self-Organizing Map (SOM) is an unsupervised machine learning technique which has been shown to be successful in the mapping of high-dimensional data into a 2D representation referred to as a map. These maps are easier to interpret and aid in the classification of data. In this work, the existing algorithms for the SOM have been extended to generate 3D maps. The higher dimensionality of the map provides for more information to be made available to the interpretation of classifications. The effectiveness of the implementation was verified using three separate standard datasets. Results from these investigations supported the expectation that a 3D SOM would result in a more effective classifier.

ISBN: 9781321318166Subjects--Topical Terms:

515831
Mathematics.

Self-organizing maps and galaxy evolution.
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020 $a 9781321318166
035 $a (MiAaPQ)AAI1568591
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040 $a MiAaPQ $c MiAaPQ
100 1 $a Beland, Jacques. $3 3169267
245 1 0 $a Self-organizing maps and galaxy evolution.
300 $a 138 p.
500 $a Source: Masters Abstracts International, Volume: 54-01.
500 $a Advisers: Sabine McConnell; Judith Irwin.
502 $a Thesis (M.S.)--Trent University (Canada), 2015.
520 $a Artificial Neural Networks (ANN) have been applied to many areas of research. These techniques use a series of object attributes and can be trained to recognize different classes of objects. The Self-Organizing Map (SOM) is an unsupervised machine learning technique which has been shown to be successful in the mapping of high-dimensional data into a 2D representation referred to as a map. These maps are easier to interpret and aid in the classification of data. In this work, the existing algorithms for the SOM have been extended to generate 3D maps. The higher dimensionality of the map provides for more information to be made available to the interpretation of classifications. The effectiveness of the implementation was verified using three separate standard datasets. Results from these investigations supported the expectation that a 3D SOM would result in a more effective classifier.
520 $a The 3D SOM algorithm was then applied to an analysis of galaxy morphology classifications. It is postulated that the morphology of a galaxy relates directly to how it will evolve over time. In this work, the Spectral Energy Distribution (SED) will be used as a source for galaxy attributes. The SED data was extracted from the NASA Extragalactic Database (NED). The data was grouped into sample sets of matching frequencies and the 3D SOM application was applied as a morphological classifier. It was shown that the SOMs created were effective as an unsupervised machine learning technique to classify galaxies based solely on their SED. Morphological predictions for a number of galaxies were shown to be in agreement with classifications obtained from new observations in NED.
590 $a School code: 0513.
650 4 $a Mathematics. $3 515831
650 4 $a Computer Science. $3 626642
650 4 $a Physics, Astrophysics. $3 1671120
690 $a 0984
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710 2 $a Trent University (Canada). $b Applied Modeling and Quantitative Methods. $3 2101153
773 0 $t Masters Abstracts International $g 54-01(E).
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791 $a M.S.
792 $a 2015
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1568591