We apply and compare various Artificial Neural Network (ANN) and other
algorithms for automatic morphological classification of galaxies. The ANNs are
presented here mathematically, as non-linear extensions of conventional
statistical methods in Astronomy. The methods are illustrated using different
subsets Artificial Neural Network (ANN) and other algorithms for automatic
morphological classification of galaxies. The ANNs are presented here
mathematically, as non-linear extensions of conventional statistical methods in
Astronomy. The methods are illustrated using different subsets from the ESO-LV
catalogue, for which both machine parameters and human classification are
available. The main methods we explore are: (i) Principal Component Analysis
(PCA) which tells how independent and informative the input parameters are.
(ii) Encoder Neural Network which allows us to find both linear (PCA-like) and
non-linear combinations of the input, illustrating an example of unsupervised
ANN. (iii) Supervised ANN (using the Backpropagation or Quasi-Newton
algorithms) based on a training set for which the human classification is
known. Here the output for previously unclassified galaxies can be interpreted
as either a continuous (analog) output (e.g. T-type) or a Bayesian {\it a
posteriori} probability for each class. Although the ESO-LV parameters are
sub-optimal, the success of the ANN in reproducing the human classification is
2 T-type units, similar to the degree of agreement between two human experts
who classify the same galaxy images on plate material. We also examine the
aspects of ANN configurations, reproducibility, scaling of input parameters and
redshift information.Comment: uuencoded compressed postscript. The preprint is also available at
http://www.ast.cam.ac.uk/preprint/PrePrint.htm
