Документ взят из кэша поисковой машины. Адрес оригинального документа : http://star.arm.ac.uk/~cwr/docs.html
Дата изменения: Mon Jan 8 00:44:01 2007
Дата индексирования: Mon Oct 1 23:09:14 2012
Кодировка:

Поисковые слова: п п п п п п п р п р п р п р п
Armagh Observatory: Chris Winter [ Documents ]
Home Thesis Code ( Docs ) OfficeCam Bookmarks

 

Document Repository

Here you will find various documents I have written, or assisted in the writing of. They have been organised into a number of convenient categories:

 

Reports

Progress Report, 2003
[ pdf ] [ ps ]
Title: Automated Spectral Analysis

Description: At the end of every PhD student's first year, a 5000 word report must be submitted for the purposes of differentiation (i.e. will They let you stay, or show you the door?). This is my report.

 

Papers

Winter, Jeffery & Drilling, 2003
[ pdf ] [ ps ]

BibTeX Reference

Title: Automatic Classification of Subdwarf Spectra Using a Neural Network

Description: Paper version of a poster presented at the Extreme Horizontal Branch Stars and Related Objects conference, held at Keele University 16 - 20 June 2003.

Abstract: We apply a multilayer feed-forward back propagation artificial neural network to a sample of 380 subdwarf spectra classified by Drilling et al. (2002), showing that it is possible to use this technique on large sets of spectra and obtain classifications in good agreement with the standard. We briefly investigate the impact of training set size, showing that large training sets do not necessarily perform significantly better than small sets.

Status: Awaiting publication in the EHB conference proceedings.

 

Posters

Winter, Jeffery & Drilling, 2003
[ ps ] [ jpg ]
Title: Automatic Classification of Subdwarf Spectra Using a Neural Network

Description: Poster presented at the Extreme Horizontal Branch Stars and Related Objects conference, held at Keele University 16 - 20 June 2003.

Abstract: We apply a multilayer feed-forward back propagation artificial neural network to a sample of 380 subdwarf spectra classified by Drilling et al. (2002), showing that it is possible to use this technique on large sets of spectra and obtain classifications in good agreement with the standard. We briefly investigate the impact of training set size, showing that large training sets do not necessarily perform significantly better than small sets. Plans for future work in this area are also outlined.

 

 

© Chris Winter, 2007
cwr at arm dot ac dot uk