NASA/IPAC EXTRAGALACTIC DATABASE
Date and Time of the Query: 2019-08-22 T22:35:05 PDT
Help | Comment | NED Home

For refcode 2004MNRAS.349...87D:
Retrieve 3 NED objects in this reference.
Please click here for ADS abstract

NED Abstract

Copyright by Royal Astronomical Society. 2004MNRAS.349...87D Machine learning and image analysis for morphological galaxy classification de la Calleja, Jorge; Fuentes, Olac Abstract. In this paper we present an experimental study of machine learning and image analysis for performing automated morphological galaxy classification. We used a neural network, and a locally weighted regression method, and implemented homogeneous ensembles of classifiers. The ensemble of neural networks was created using the bagging ensemble method, and manipulation of input features was used to create the ensemble of locally weighed regression. The galaxies used were rotated, centred, and cropped, all in a fully automatic manner. In addition, we used principal component analysis to reduce the dimensionality of the data, and to extract relevant information in the images. Preliminary experimental results using 10-fold cross-validation show that the homogeneous ensemble of locally weighted regression produces the best results, with over 91 per cent accuracy when considering three galaxy types (E, S and Irr), and over 95 per cent accuracy for two types (E and S). Keywords: methods: data analysis, galaxies: fundamental parameters
Retrieve 3 NED objects in this reference.
Please click here for ADS abstract

Back to NED Home