Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Haar Scattering Networks in Pattern Recognition: A promising
  approach   The aim of this paper is to discuss the use of Haar scattering networks,
which is a very simple architecture that naturally supports a large number of
stacked layers, yet with very few parameters, in a relatively broad set of
pattern recognition problems, including regression and classification tasks.
This architecture, basically, consists of stacking convolutional filters, that
can be thought as a generalization of Haar wavelets, followed by non-linear
operators which aim to extract symmetries and invariances that are later fed in
a classification/regression algorithm. We show that good results can be
obtained with the proposed method for both kind of tasks. We have outperformed
the best available algorithms in 4 out of 18 important data classification
problems, and have obtained a more robust performance than ARIMA and ETS time
series methods in regression problems for data with strong periodicities.

31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org