Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Artificial Neural Networks as Non-Linear Extensions of Statistical
  Methods in Astronomy   We attempt to de-mistify Artificial Neural Networks (ANNs) by considering
special cases which are related to other statistical methods common in
Astronomy and other fields. In particular we show how ANNs generalise Bayesian
methods, multi-parameter fitting, Principal Component Analysis (PCA), Wiener
filtering and regularisation methods. Examples of morphological classification
of galaxies illustrate how non-linear ANNs improve on linear techniques.

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The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
1
The categories of this paper are: cs
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics